Xinxin Sun
email: asunxinxin@cau.edu.cn

Longqing Sun

Yiyang Li

A Decision Model Forlive Pig Feeding Selection

Keywords: live pig breeding, nutrients prediction, daily nutrition, feeding quantity, precision feeding, nutrition requirement

With massive and intensive developmentof Chinese live pig industry, the imbalance of feeding nutritionturns out to be a primary problem. The main reason is that the nutrition needs to be adaptive tolive pig's exact growth conditions in production management process. Regarding this, the paper aims at developing a feedingselection model for pig breeding based on major nutrients predictive model proposed in the Nutrient Requirements of Swine published by U.S. NRC in 1998. The objective of this model is to achieve a minimum cost of feeding stuff under the premise that the nutrients satisfy pig's needs. According to values proposed in NRC and nutritional elements data, a feedingselection model was developedto give proper suggestions on feeding dietary nutrients as well as exact type and quantity of pig feeding stuff. The work presented in this paper would contribute to the optimal precision feeding strategies so as to guarantee live pig growth quality in management process.

Introduction

The main cost of live pig feedingcomes from the pig feeding stuffcost, accounting for 60-70% of the total cost of the large-scale pigfeeding, and accounting for 70-80% of the total cost of pig specialized households [START_REF] Chen | Study on Nutrient Diagnosis and Diet Formulation System of Swine[END_REF], higher than labor costs (14 -22%) and piglets costs (18 -35%) [START_REF] Yu Wen | Chinese farmers backyard pig production cost element analysis[END_REF].There is at least 10% of the feeding stuff waste every year [START_REF] Zhao Xianwu | How to improve the growing and finishing pigs weight gain and feed efficiency[END_REF], result infeeding costs continue to rise, limiting our economic development of pig feeding and polluting the environment [START_REF] Xiong Yuanzhu | Pig Industry Technology Review and Prospects[END_REF]. In the condition of ensuring the healthy growth of pigs, depending on the growing season, timing and quantitative feeding, fully tapping the feeding stuff nutrient availability, and improvingfeeding stuff conversion rate, are the key to reducefeeding costs and improve feeding efficiency.

From the live pig industry development status and trends of United States and Europe, there is a large systematic study in precision feeding aspects. In Denmark, pig daily food intake is controlled by computer, depending on the growing season, timing and quantitative feeding, pig breedingachieve information andstandardization [START_REF] Chen Guoyu | European pig inspection experience[END_REF]. The Danish manure normative system(DMNS) provide Danish farmers and authorities with tools for fertilizer planning control,the system includesthat dietary nutrient content, nutrient digestibility, feeding stuff intake and nutrient retention in the pig body. According to those informationthe system included, calculatingthe standard value of nutrients excreted, then getting the feeding stuff conversion rate, andproviding the basis for the next day grain feeding. In European countries, the feeding stuff conversion rate ofDanish is the standard to correcttheir feeding stuff conversion rate [START_REF] Gu Zhaobing | Status quo and development trend of foreign pig industry[END_REF].In the Netherlands, the farming management software(Agrovision FARM)and the intelligent sow management system(Velos)are been used in pig feeding [START_REF] Yi Ge | Intelligent pig management[END_REF]. Agrovision FARM can record all important, true and reliable farming information. Velos can afford precise pig feedingin accordance with the feeding curve, avoiding feedwastage caused by artificial feeding and the pig body conditionuneven caused by feeding inaccurate, thus ensuring the most accurate for every pig feeding. In the United States,the Nutrient Requirements of Swine which was published by U.S. NRC in 1998,providing a reliable basis for pigfeeding. According to different stages of pig breeding standards, U.S. researchers designed different feed formulation to reduce nutrient waste and manure nitrogen emissions, save feeding costs [START_REF] Lei Minggang | Swine production technology development of new trends[END_REF]. By using the technology of RFID radio frequency identification, wide network video surveillance, reach the intelligentpig feeding [START_REF] Gu Zhaobing | Status quo and development trend of foreign pig industry[END_REF].The use of automatically phased system makes pig feeding stuff conversion more efficient, feed fully compatible with the growth rate of pigs, neither because of lacking feeding stuff nutrients affect the growth rate of pigs, not because of excess nutrients in feed resultin wastage. In our country,for the differences between China and the U.S. in terms of pig breeding species and breeding conditions, Fu Linsheng, Xiong Benhai and other researchers developed pig nutrition requirements dynamic forecasting system based on the NRC swine dynamic nutrition requirements,through appropriately adjusting the parameters to match the characteristics of Chinese pig breeding [START_REF] Fu Linsheng | Development of DynamicProjection SystemforNutrient Requirements of Swine[END_REF].The system provide basic utility for a variety of pigs diet formulation designing under certain condition nutrient requirements, rationally using feed materials, and reducing feed costs and environmental pollution. The study of thissystem promotesthe fine breeding,but the choice of the kinds of feeding stuffand feeding ration is not yet involved.

For the problem of low feedingutilization rate and high breeding costinthe development of aquaculture technology, according to NRC modeling software, the objective of this study was to calculate pig daily nutritional requirements in a growth phase, based on the type of feed and the daily requirement, establishing feeding portfolio optimization model, obtainingthe mixed feed and mixed proportions by solving the model, improve feed utilization, reduce breeding costs, achieveprecise feedingof pigbreeding.

Method

Daily requirement

Pig daily nutritional requirements is an important basis for the dailydesign, the pig nutritional requirements the U.S. National Research Council (NRC) recommended is considered the most authoritative pig standards. It uses a mathematical model to estimate the integral energy, protein, amino acids,minerals and vitamins growing pigs needed [START_REF] Fu Linsheng | Development of DynamicProjection SystemforNutrient Requirements of Swine[END_REF].

NRC is use of three interrelated nutritional needs modes, growth model, pregnancy model and lactation model, to software calculate. In this paper, according to the growth model selectingGrowing -Finishing Pigsas the study object, the main two growing stages are 25-38kg and 38-57kg. Inputting the data pig's weight, type, energy indicators and so on, outputtingthe nutritional requirements data, which includes digestible energy (DE)-based energy, amino acid requirements, minerals and vitamins requirement. In the calculation, choosing nutrition requirements model of growth model to software calculate, the data obtained by NRC software model calculating shown in Table 1,Table 2,Table 3. Data in the table above are the main basis for this paper feeding selection. All selection, recipe selection must meet the growth needs, and the data obtained through the NRC modeling software are the requirements must be meet, that is theconstraints in the process of forecast calculation.

According to the number of nutritional requirements and cost, select the most appropriate feeds to feed.

Feeding decision model

With the lowest cost as the objective function, the linear programming mathematic model was built [18]. Suppose there are 𝑛 kinds of feeds to choose from for a growth phase,𝑚 𝑖 represents the 𝑖th feed, 𝑚 𝑖 𝑚𝑎𝑥 and 𝑚 𝑖 𝑚𝑖𝑛 represent the max andmin limits of feed, 𝑖 as the nutrients necessary number for the growth of pigs, 𝑛 𝑗 represents the 𝑗 th nutrient element,𝑛 𝑗 𝑚𝑎𝑥 and𝑛 𝑗 𝑚𝑖𝑛 represent the max intake of nutrient and min intake of nutrients 𝑛 𝑗 ;in unit mass of 𝑚 𝑖 , the mass percentof nutrients 𝑛 𝑗 denoted 𝑢 𝑖𝑗 ; the market price offeed 𝑚 𝑖 recorded as 𝑐 𝑖 , the number of daily feed 𝑚 𝑖 as 𝑥 𝑖 , feeding costs can be expressed as the objective function is shown as follows:

Z = 𝑚𝑖𝑛 ∑ 𝑐 𝑖 𝑥 𝑖 𝑛 𝑖=1 ( 𝑖 = 1,2, … … , 𝑛)(1)
WhereZ represents the cost of feed fed, 𝑐 𝑖 is the𝑖th feed market price, 𝑥 𝑖 represents the 𝑖th day ration of 𝑚 𝑖 feed.

Feed usage constraints [( 2) -( 4)]:

𝑚 𝑖 𝑚𝑖𝑛 ≤ 𝑥 𝑖 ≤ 𝑚 𝑖 𝑚𝑎𝑥 ( 𝑖 = 1,2, … … , 𝑛)(2)
Where𝑥 𝑖 represents the 𝑖th day ration of 𝑚 𝑖 feed, 𝑚 𝑖 𝑚𝑎𝑥 and 𝑚 𝑖 𝑚𝑖𝑛 represent the max and min limits of feed.

𝑛 𝑗 𝑚𝑖𝑛 ≤ ∑ µ 𝑖𝑗 𝑥 𝑖 𝑛 𝑖=1 ≤ 𝑛 𝑗 𝑚𝑎𝑥 ( 𝑖 = 1,2, … … , 𝑛) (3)
Where𝑛 𝑗 𝑚𝑎𝑥 and𝑛 𝑗 𝑚𝑖𝑛 represent the max intake of nutrient and min intake of nutrients 𝑛 𝑗 ; 𝑛 𝑗 represents the 𝑗th nutrient element; 𝑢 𝑖𝑗 is the mass percent of nutrients 𝑛 𝑗 denoted; the number of daily feed 𝑚 𝑖 as 𝑥 𝑖 .

𝑥 𝑖 ≥ 0( 𝑖 = 1,2, … … , 𝑛)(4)

Where𝑥 𝑖 represents the 𝑖th day ration of 𝑚 𝑖 feed. Though model computing can obtained 𝑥 𝑖 as well as get quality for all kinds of feed, while able to calculate the value of Z, which is the minimum cost of feed mix in𝑥 𝑖 situations.

Data flow

Choose raising methods

Choose the growth stage nutritional needs calculated via NRC buy feeding stuff?

Home

Fig1. Pig feeding selection decision process

The feeding selection decision process is illustrated in Figure 1, the model processes are listed below: 1. Determine the growth stage of pigs, getting pig weight data. 2. After knowing the weight using NRC modeling software calculate nutritional requirements the pig needed, which includes the digest energy (DE)-based energy, amino acid requirements, minerals and vitamins requirement. 3. Meeting the nutritional needs(get from 2) conditions, can purchased directly competing mixed feeding stuff, can also be mixed in accordance with the existing feeding stuff themselves ,if mixed by themselves, determining the type of breeding materials and ingredients contained in the existing ratio. 4. Known nutritional requirements data and raw data feed decision to build a mathematical model to calculate the lowest cost feeding stuff mix, that kind of feed needed, how much needs. 5. If it satisfies the minimum cost and to meet the nutritional needs, you get to choose the end result of the calculation, if not,loop to 3 recalculate until getting the outcome, the calculation ended.

Results and discussion

Feeding Selection (1) Data analysis

In this section, through example represent the feeding decision mathematical model simulation process, accordingto the conditions of meeting the growth needsof pig, with the existing status quo forage species breeding farms, pigs breeding mixed feeding stuff is calculated. Thereare five different options pig feed ,such as A, B, C, D, E, the feed information as shown in Table 4. (2) Modeling Under theconditions of meeting the nutrients requirements of pig growth, in order to minimize the cost of keeping the goal to establish programming model ,such as the ratio of the feed formula (5-10) below.

Objective function:

𝑍 𝑚𝑖𝑛 = 7𝑥 1 +6.2𝑥 2 +6.3𝑥 3 +5.5𝑥 4 +5.2𝑥 5 (5)
Where the objective function is to get the cost of the optimal feed ration, 𝑍 𝑚𝑖𝑛 is the feeding stuff ration cost; 𝑥 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E are five different pig feeding stuff ration , 𝑥 𝑖 corresponds to the 𝑖th feeding stuff ration.

Satisfy the following constraints established:

280𝑥 1 + 145𝑥 2 + 160𝑥 3 + 310𝑥 4 + 150𝑥 5 ≥ 141 ( 6
)
Where the protein are the growth feed pigs needs must be meet of feeding process,𝑥 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E five different pig feeding stuff ration (𝑥 𝑖 ≥0,𝑖= 1,2,3,4,5;).

100𝑥 1 + 700𝑥 2 + 200𝑥 3 + 100𝑥 4 + 100𝑥 5 ≥ 89.49 (7)
Where the crude fiber is the growth feed pigs needs must be meet of feeding process,𝑥 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E five different pig feeding stuff ration (𝑥 𝑖 ≥0,𝑖= 1,2,3,4,5;). 9.9𝑥 1 + 10𝑥 2 + 9.5𝑥 3 + 6𝑥 4 + 6.5𝑥 5 ≥ 3.54 [START_REF] Yi Ge | Intelligent pig management[END_REF] Where the vitamins are the growth feed pigs needs must be meet of feeding process,𝑥 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E five different pig feeding stuffration (𝑥 𝑖 ≥0,𝑖= 1,2,3,4,5;).

45𝑥 1 + 80𝑥 2 + 70𝑥 3 + 50𝑥 4 + 10𝑥 5 ≥ 11.31 (9)
Where the calcium are the growth feed pigs needs must be meet of feeding 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E five different pig feeding stuff ration (𝑥 𝑖 ≥0,𝑖= 1,2,3,4,5;).

8𝑥 1 + 10𝑥 2 + 9𝑥 3 + 11𝑥 4 + 5𝑥 5 ≥ 5.26 ( 10
)
Where the phosphorus must be meet the growth feeding pigs needs during the feeding process,𝑥 1 , 𝑥 2 ,𝑥 3 , 𝑥 4 , 𝑥 5 represent A, B, C, D, E five different pig feeding stuff ration (𝑥 𝑖 ≥0, 𝑖= 1,2,3,4,5;).

(3) Result

By decision model formula (5-10) constraint equation knowing that, if breeding farms choose to feedfeeding stuffA 0.0586g per pig per day, feeding stuffB 0.0762kg, feeding stuffD 0.3663kg, to meet the growing needs of pigs ,and investment the lowest total cost of 2.9 Yuan.

3.2Analysis

From the simulation knowing that, according to the nutritional requirements datawhich are shown in tables (1-3), using the mathematical models to calculate to get all kinds of decision-mixed feeding stuff requirements, mixed feeding stuff (Mix) combinations are shown in Table 5. The mixed feeding stufffeedingis better than one feeding stuff feeding, such as A, B, C, D, E are five different methods of feeding pig feeding stuff alone, in the respects of feeding stuff costs and feeding ration., A, B, C, D, E, and Mix feeding volume and feeding cost comparison data are shown in Table 6. Analysis showed that, when a singlefeeding stuffbreeding meet all the nutritional needs,some nutritional elements areexcess result in feeding stuff waste. When nutrients to meet the requirements of crude fiber, protein and phosphorus ration greater than requirements, indirectly feeding stuff waste and excess nutrients polluting the environment during defecation. Selecting mixedfeeding stuff fed to reduce breeding costs and waste of resources, and with a more balanced nutritional needs.

Table5Calculated

3.2.1Price factor

In this paper, the model aims at givingthe lowest cost of feeding stuff feeding combination in the breeding process, depending on the kind of prices of feeding stuff. The changing feed prices on the marketimpact model calculations for changes in feeding stuff prices, using the simulation models in terms of price changesfeeding stuff case to get the selection result. For A, B, C, D, E the five kinds of pig feeding stuff,listingthree groups changing price( Ⅰ , Ⅱ , Ⅲ ). Price Ⅰ {7,6.2,6.3,5.5,5.2}; PriceⅡ{7.0,6.9,6.5,6.5,5.7}; PriceⅢ {7.5,6.9,6.5,6.7,4.5}. Calculated by the model, the results obtained 3 Group Mixed Feed Mix Ⅰ, Mix Ⅱ, Mix Ⅲ, as the Table 7shown. Themodel results were analyzed, and the three groups ofmixed feeding stuff costs were compared with a single feeding costs (Table 6), we find that the mathematical model of mixed feeding stuff to get the lowest cost, shown in Figure 2. The priceⅠ, priceⅡ, priceⅢthree groups of different prices feeding stuff, mixed feeding stuff Mix cost obtained (shown in black bars shown in block) are minimum by solving the model, the calculation results show that the three groups of computing results, the selection of only one feeding stuff to breed, under the conditions required to meet the nutritional needs of feeding stuffcosts were greater than the cost it takes to Mix, price changes in conditions, the model still can calculate the lowest-cost breeding selection results.

Table7Calculated results of feeding decision model

Fig. 2Comparison of cost between mixed and single feeding stuff

3.2.2Specified mixed feeding stuff

Under certain circumstances, breeding pigfeeding stuff with special requirements, for the same growth stage pig,due to the differentgrowth status the needs of pig feeding stuff different,poor growth conditions may need to be bred a certain or a few specific pig feed feeding stuff, different growth stage with different nutritional requirements , feeding the specific needs of different feeding stuff, in order to meet these conditions, in particular the feeding stuff of feed A and feeding stuff of fed specific B, D, use the model to calculate the breeding selection decision, the results shown in Table 8. The results of the data analysis showed that under certain circumstances the feeding stuff ration, the model calculated the lowest cost of mixed feed, shown in Figure 3.Feeding stuffA in the feeding of two specific and particular conditions of feeding stuff B of A, B, C, D, E, Mix these six kinds of feeding costs compared to Figure 3,breeding feeding stuff A group known specific investment Mix Hey cost of 3.70 Yuan, the specific feed fed group B, D Mix feeding cost of 3.71 Yuan, the lowest point are in the fold, the minimum cost of breeding, feeding fixed feeding stuff under specific conditions, to get through the model to calculate the most cost excellent choice of feeding stuff result.

3.2.3Nutritional needs

In the process of pig breeding, different season with different effects on pig growth, higher temperatures summer and low temperatures winter have different effects on pig feeding stuff intake and nutrient requirements. Summer reduce the needs for appropriate high crude fiber intake of growth pig, and winterlow temperatures need to increase energy intake of protein for pig , which is required to change the conditions of the model equation constraint. In summer and winter the two conditions, for example, through a decision-making model for solving the feed mixed feeding stuff to get the results shown in Table 9. Though the results of the analysis ,we know that in the summer and winter both cases, the results calculated by the model are bred in to meet the nutritional needs of growing conditions, day feeding stuff costs 2.9Yuan and 3.3Yuan, comparing with a separate feeding A, B, C , D, E five kinds of feed costs necessary 6.2,6.0,5.5,4.8,4.9 (Yuan) , is the optimal cost of the pig breeding, that seasonal changes in nutritional requirements change of pig growth conditions, the model calculated optimal costs mixed feeding stuff.

Conclusion

The aim of this paper is to build a pig feeding optimization mathematical model and obtain the lowest-cost feeding mixed results.A model was developed based on optimal cost, considering three elements: price factor, specified feeding stuff and nutritional requirements. The results were analyzed demonstratingits validity according to growing pig conditions. Use of the feeding selection decision model can enable more precise feeding, proper selection of feeding stuff,lower cost and increasing economic benefit of pig breeding.

  , feed a pig need spendinga day

Fig. 3

 3 Fig.3 Comparison of cost between specific feeding A and feeding B, D

Table 1 Daily energy intakefor each pig

 1

	Body	Total	Digestibl	Predictio	Lipi	Crud	Vitami		Calciu	Phosphoru
	weight	energ	e		n		d	e	n		m (g)	s
		y		energy	(g)	(g)	fiber	(IU)		(g)
		(kg)		(IU)				(g)
	25-38(kg)	1.451	4406	119	128	69.65 1926.5	9.00	4.19
	*									1
	38-57(kg)	1.904	5901	141	194	89.49 2609.6	11.31	5.26
	*									5
	Body	Sodiu	Chlori	Magnesi	Potassi	Copp	Iodi	Iron	Zin	Mangan	Seleni
	Weight	m	de (g)	um		um	er	ne	(mg	c	ese	um
		(g)			(g)		(g)	(mg)	(mg)	)	(m	(mg)	(mg)
											g)
	25-38(	1.35		1.06	0.53		3.25	5.78 0.18 89.1	80	2.64	0.269
	kg)									8
	38-57(	1.82		1.43	0.71		3.70	6.57 0.25 95.7	98	3.57	0.31
	kg)									4		2
	Notes:*25-38(kg),*38-57(kg), mean sub-stages weight of growth pigs.

Notes:*25-38(kg), *38-57(kg), mean sub-stages weight of growth pigs.

Table2 Daily trace elements intakefor each pig

Table 3 Daily vitamin intakefor each pig

 3

	Body Weight	A	D	E	K	B 6	B 12
		(IU)	(IU)	(IU)	(mg)	(mg)	(μg)
	25-38(kg)	1714	198	14.51	0.66	1.32	13.7
	38-57(kg)	2322	268	19.65	0.89	1.79	13.35

Notes:*25-38(kg), *38-57(kg), mean sub-stages weight of growth pigs.

Table 4

 4 Proportion of the energy in the feeding stuff

	feeds		Prediction	Crude	Vitamin	Calcium	Phosphorus	Price
		energy	(g/kg)	fiber	(mg/kg)	(g/kg)	(g/kg)	(yuan/kg)
		(IU)		(g/kg)
	A	3050	280	100	9.9	45	8	7
	B	3060	145	700	10	80	10	6.2
	C	3050	160	200	9.5	70	9	6.3
	D	3050	310	100	6	50	11	5.5
	E	3060	150	100	6.5	10	5	5.2

  mixed feeding stuff(Mix)

	D	0.87	4.8
	E	0.94	4.9
	Mix	0.50	2.9
	Note:*Feed ration, feeds a pig need a day; *Cost, feed per
	pig need spendingper day
	Selected feeds	Feed ration*(kg)	Cost*
			(yuan)
	A	0.0586	0.4
	B	0.0762	0.5
	D	0.3663	2.0
	Note:*Feed ration, feeds a pig need a day; *Cost, feed a pig
	need spendinga day
	Table6Comparisons of intake and cost between mixed and singlefeeding stuff
	Fed feeds	Feed ration* (kg)	Cost*
			(yuan)
	A	0.89	6.2
	B	0.97	6.0
	C	0.88	5.5

Table 8Calculated

 8Calculated

	results of feeding decision model
	Mix feeds	selection feeds	Costs*
			(yuan)
	Specific A	A、	3.70
	Specific B、D	B、C、D、E	3.71
	Note:*Cost, feed a pig need spendinga day

Table 9

 9 Calculated results of feeding decision model

	Constrains	Selection	Costs
		feeding stuff	(yuan)
	Winner	A、B、D	2.90
	Summer	B、D	3.3
	Note:*Cost, feed a pig need spendinga day

Acknowledgement

This paper was supported by the Shandong Province innovation special (2014XGA13054).Supported by National Science and Technology Supporting Program (2014BAD08B05).


Yande Liu

Yuxiang Zhang

Lixia Jiang

Haiyang Wang

Determination of lead (Pb) content in Vetiver grass roots by Raman spectroscopy

Keywords: Words: Heavy metal, Vetiver grass, Raman spectroscopy

In order to provide references for heavy metals diagnosis of Vetiver grass, Raman spectroscopy technology was employed, the partial least squares (PLS) quantitative analysis model of heavy metal lead of Vetiver grass root was established, different processing methods were used to optimize the Raman spectra of Vetiver grass roots, Successive projection algorithm (SPA) was applied to screen the bands of Raman spectrum and enhance the model's accuracy. The best Raman spectroscopy quantitative analysis model of lead content in Vetiver grass root were set up, 20 unknown samples were used to test the quality of optimized model. The prediction correlation coefficient and the root mean square error were 0.607 and 0.040 g/kg, respectively.

INTRODUCTION

As a kind of not easily biodegradable toxic substances, heavy metals are harmful to the vast majority of biology, especially the human. However, heavy metals can be gradually accumulated in plants and animals by the food chains [START_REF] Devkota | Accumulation of heavy metals in food plants and grasshoppers from the Taigetos Mountains, Greece[END_REF]. Ingestion of heavy metals in agricultural products is one of the important factors that cause the accumulation of heavy metal in human body [START_REF] Arora | Heavy metal accumulation in vegetables irrigated with water from different sources[END_REF]. Therefore, the content of heavy 1 *Corresponding author. E-mail: jxliuyd@163.com metals in foods is an important index for food safety risk assessment. In order to reduce the harm caused by eating crops, which containing excessive heavy metals, seeking a kind of rapid and nondestructive method for detecting heavy metal content in plants is necessary.

However, the traditional methods for detecting heavy metal content in plants, including X-ray fluorescence spectroscopy (XRF) [START_REF] Donner | X-ray absorption and micro X-ray fluorescence spectroscopy investigation of copper and zinc speciation in biosolids[END_REF], inductively coupled argon (ICP) [START_REF] Tormen | Rapid assessment of metal contamination in commercial fruit juices by inductively coupled mass spectrometry after a simple dilution[END_REF] and atomic absorption spectroscopy (AAS) [START_REF] Tm. Ufekci | Determination of Pb (II), Zn(II), Cd(II), and Co(II) ions by flame atomic absorption spectrometry in food and water samples after preconcentration by coprecipitation with Mo(VI)-diethyldithiocarbamate[END_REF] and so on are always combined with digestion and chemical analysis instrument, which are time-consuming and plant-damage [START_REF] Zhen | Contamination of heavy metals in soil present situation and method[END_REF]. Raman spectroscopy, which is a spectrum analysis technology with the advantages of fast, non-destructive, non-pretreatment, has the board application prospect in terms of detecting heavy metal contents in plants indirectly [START_REF] Torreggiani | Raman study of in vivo synthesized Zn(II)-metallothionein complexes: Structural insight into metal clusters and protein folding[END_REF]. Although the pure heavy metals cannot be estimated directly by Raman spectroscopy, heavy metals in plants usually combined with the organic molecular groups that have Raman spectral information [START_REF] Tan | Molecular mechanisms of plant tolerance to heavy metals[END_REF]. Therefore heavy metals in plants can be indirectly detected by using spectral technique basing on the chelation.

Vetiver grass can live in the range of -22 ℃ to 55 ℃ temperature, which has strong vitality. It also can grow up in the environment of heavy metal pollution, shows the strong tolerance of heavy metals [START_REF] Pang | Physiological aspects of vetiver grass for rehabilitation in abandoned metalliferous mine wastes[END_REF][START_REF] Smith | Vetiver grass hedges for erosion control on a cropped flood plain: hedge hydraulics[END_REF]. Because of these characteristics, Vetiver grass becomes one of the ideal plants for soil and water conservation and phytoremediation of soil heavy metal pollution [START_REF] Datta | Phytoremediation potential of vetiver grass [chrysopogon zizanioides (L.)] for tetracycline[END_REF].

MATERIALS AND METHODS

Samples and reagents

The Vetiver grasses in this study were collected in the heavy metal pollution fields of Guixi city, China. The ultrapure water was produced by ultrapure water polishing system (TS-RO-10L/H, TAOSHI Water Equipment Engineering Co. Ltd., China), with 18.2 MΩ•cm resistivity. All chemical reagents (HNO 3 , HClO 4 ) in this study are of analytical purity grade, and purchased from Sinopharm chemical reagent Co. Ltd., China.

Samples preparation

The vetiver grass were washed by tap water until removing the soil and dust on the surface of plants, and rinsed by ultrapure water twice in order to remove the interference of heavy metals in water. Then the roots of vetiver grass were cut by scissors for the samples for Raman spectroscopy detection.

Raman spectral measurement

All Raman spectra were measurement from 90 cm -1 to 3500 cm -1 with a confocal microprobe Raman spectrophotometer (SENTERRA, Bruker, Germany). Each root sample of Vetiver grass was put on the quartz glass slide, which has been put on the sample frame of spectrophotometer. Eyepiece of 10 times and objective lens of 50 times were employed respectively. In order to make the laser focus on the internal organizations of Vetiver grass roots, fine tune the sample frame until the clearest images of plant surface was observed.

At the same time, the impact to organizations of Vetiver grass roots by the quantity of heat of laser should be considered. Although the higher power laser can improve the spectrum information, it also can be more easily destroy the organizations, which would influence the outcome of Raman spectral measurement. Choose the optimal instrument experimental parameters through several tests to ensure that the plant in the laser focus will not lead to excessive heat structure of plant tissue damage: the excitation wavelength was 785 cm -1 , the Resolution was 0.5 cm -1 , the integration time was 5s, the light spot diameter was 50*1000 microns, and the power of laser was 10 mw.

In order to reduce the spectral measurement error, each root sample of Vetiver grass was measured three times, so each spectrum of root sample of Vetiver grass was averaged from three parallel measurements.

Measurement of quality parameters

All root samples of Vetiver grass were dried to constant weight by drying oven (DHG-9101-19A, Shanghai Hong Electronic Technology Co. Ltd., China) with temperature of 75 to 80 centigrade after Raman spectral measurement, then ground into powder respectively with a pulverizer.

Each Vetiver grass root powder sample was weighed 0.2g by electronic balance, soaked with 10ml HNO 3 in the flask and sealed overnight, then digested by an electric hot plate (Mb, Beijing Kewei Yongxing Instrument Co. Ltd., China) until the large granular sediments were nearly disappeared. The flask was removed with the crucible tongs for cooling, 10 mL HNO 3 and 5 mL HClO 4 standard solution were added into the flask respectively after cooling, then heated by electric hot plate again until white smoke appeared in the flask. Removing the flask for cooling again, the sample solution in the flask was washed into 25 ml volumetric flask by deionized water, and then filtered into another 25 mLvolumetric flask. Finally, flame atomic absorption spectrometer (AA280FS, Varian, America) was used for measuring the total lead in solution of Vetiver grass roots.

RESULTS AND DISCUSSION

Raman spectral features of Vetiver grass root

All Raman spectrums of Vetiver grass root samples were averaged to be a mean Raman spectrum, and shown in Fig. 1. Because of the low power of laser, the Raman signal of Vetiver grass roots is very weak, the Raman peak are not obviously. What's more, the weak Raman characteristic peaks were covered up by the interfering substance in the plants and the fluorescence effect should be another main reason for causing this phenomenon.

C=O stretch cyclobutanone

At the same time, in the range of 2000 to 3500 cm -1 of Raman spectrum of Vetiver grass root, the effective Raman characteristic information in Raman spectra of Vetiver grass root does not exist. Therefore, in order to reduce the interference from other useless information, excluding this region of Raman spectrum, 150 ~ 2020 cm -1 spectral region are selected for establishing Raman mathematical analysis model of Vetiver grass root.

Reference analysis

In order to facilitate the model's modeling and testing, 60 plants were randomly selected in 80 Vetiver grass samples as a calibration set, the remaining 20 plants as prediction sets. The statistical analyses including range, mean and standard deviation (S.D.) for lead values in Vetiver grass roots are performed in Table 2. It shows that the heavy metal lead values are within the wide range for the samples of Vetiver grass roots, which are beneficial to produce robust and stable calibration models and evaluate the models' predictive ability.

Quantitative models of Vetiver grass root

Because of the weak Raman signal and the strong fluorescence intensity, a lot of Raman signal usually are covered up by the fluorescence background. Therefore, in order to establish a great Raman mathematical analysis model, enlarging the effective information in the Raman spectra and enhancing the signal-to-noise ratio of spectrum are indispensable. Different spectral preprocessing methods can effectively eliminate some interference information because of their different functions in spectrum optimization. In this study, six spectral preprocessing methods, smoothing, standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative, second derivative and baseline correction, were employed to optimize the quality of models. The quantitative partial least squares (PLS) models of different preprocessed spectrums were established with Unscrambler 8.0 software package, the PLS model of raw spectrum was also established for the sake of contrastive analysis, and shown in table 3. From the contrastive analysis of cross-validation correlation coefficients (r cv ) and cross-validation root mean square errors (RMSECV) in the table 3, it can be found that two preprocessing methods (1st derivative, 2nd derivative) have the ability to optimize the spectrum information of Vetiver grass roots, with the higher R cv and lower RMSECV than the raw spectrum. The 2nd derivative is the best preprocessing method for optimizing PLS model. However, the rest four preprocessing methods can not raise the quality of model, it should be reasonable. The main functions of derivative preprocessing method are distinguishing the overlapping peaks and improving the resolution of spectrum. It shows that the important interference factors of model established are overlapping peaks.

Identification of optimum wavelength

Each wave number of spectrum means the corresponding information of tested object. Some noises may have been collected by Raman spectrophotometer when the raman spectrums of Vetiver grass roots were collected. In order to reduce the negative influence of noises in the spectrum, eliminating the spectral ranges of noises and extracting the wavelengths of target component is necessary.

Successive projections algorithm (SPA) [START_REF] Ghasemi-Varnamkhasti | Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection[END_REF] is a forward selection method, it can promote the quality of model and ensure the realizability of model. Therefore, SPA has been widely praised in the multiple qualitative analysis of the spectrum of applications. With the help of Matlab R2010a software package, SPA was employed to solve the problem of wavelength selection after spectral preprocessing.

It can be seen from figure 2, the prediction root mean square error RMSE of model is changed along with the number of variables. In order to select the lowest prediction root mean square error, the number of selected variables of 8 was chose for establishing model. The corresponding PLS model parameters of Pb content in Vetiver grass roots was shown in table 4.

Fig.2 Variable number screening of Pb model basing on SPA

Table 4 shows that the effective variables of model was screened by SPA, the cross-validation correlation coefficients (r cv ) was rise from 0.675 to 0.697, the cross-validation root mean square errors (RMSECV) was decreased from 0.034 g/kg to 0.033 g/kg, and the principal component number (PCs) was changed to 8. Therefore, the algorithm of SPA can improve the model quality of detecting lead content in Vetiver grass roots, but the effect is not obvious.

Prediction of quality attributes with the optimum wavelength

In order to verify the feasibility of the Pb detecting models in Vetiver grass roots, external validation is further done with the 20 validation samples. The Raman scattering intensities corresponding wave numbers screened by SPA were intercepted for the variables of models, the atomic absorption values were served as measured values, and the optimized PLS models were used for prediction. Fig. 3 shows the relationships of the predicted and the measured values of the validation samples, the correlation coefficients of prediction (rp) and root mean square errors of prediction (RMSEP) of Vetiver grass root are 0.607 and 0.040 g/kg, respectively.

CONCLUSION

The calibration and validation statistics obtained in this study showed the potential of Raman spectroscopy to predict heavy metal Pb in the root of Vetiver grass. Although the accuracy of model with this method was lower than the routine analysis, Raman spectroscopy could be used as a fast and simple tool to diagnose the content of heavy metal Pb in Vetiver grass approximately. Overall, Raman spectroscopy can be used as a reference method for diagnosing the content of heavy metal Pb in Vetiver grass, but the accuracy needs to be improved.

1 Fig. 1

 11 Fig.1 Raman spectrum of Vetiver grass root

Fig. 3

 3 Fig.3 External validation correlation of heavy metal Pb content in Vetiver grass roots

Table 1 The characteristic Raman bands of Vetiver grass roots

 1

	Frequency/cm -1	vibration mode	reference compounds
	422	stretch	n-alkanes
	540	CBr stretch	alkanes
	802	ring breath	cyclohexane
	933	ring vibration	alkyl cyclobutane
	1276	N=N=N stretch	CH 3 N 3

Table 2 Statistics of heavy metal Pb in Vetiver grass roots

 2

		Minimum	Maximum	Mean
	Sample set				S.D.
		g/kg	g/kg	g/kg
	Calibration (n=60)	0.063	0.233	0.126	0.046
	Validation (n=20)	0.060	0.235	0.126	0.051
	All (n=80)	0.060	0.235	0.126	0.048

Table 3 The results of calibration and cross-validation for the Pb content in the roots of Vetiver grass samples

 3

	Pre-processing
		PCs	r c	RMSEC (g/kg) r cv	RMSECV (g/kg)
	methods
	Raw	10	0.851	0.024	0.626	0.037
	Smoothing (3)	10	0.855	0.024	0.621	0.037
	SNV	10	0.807	0.027	0.446	0.044
	1 st derivative (3)	6	0.832	0.025	0.648	0.035
	2 nd derivative (1)	10	0.773	0.029	0.675	0.034
	MSC	10	0.838	0.025	0.452	0.044
	Baseline correction	11	0.884	0.021	0.554	0.039

Table 4 The results of Pb content in the roots of Vetiver grass by PLS with SPA

 4

				RMSEC		RMSECV
	Variable selection method	PCs	r c		r cv
				g/kg		g/kg
	None	10	0.773	0.029	0.675	0.034
	SPA	8	0.758	0.030	0.697	0.033
	In order to find the compounds chelated with heavy metal lead in Vetiver grass roots, the
	characteristics of Raman spectra wavelengths screened by SPA were listed in table 5, it shows that a wide
	variety of compounds in Vetiver grass roots may be chelated with lead, including normal alkanes, bromides,

chloroalkane, allenes, hydrazone and so on.

Table 5 The characteristic Raman bands of vetiver grass roots screened by SPA

 5

	Wave number screened by
		vibration mode	reference compounds
	SPA /cm -1
	155.5
		stretch	n-alkanes
	395
			Primary alkyl bromide / Tertiary
	560	CBr stretch /CCl stretch
			chloroalkane
	614.5	CCl stretch	Para chloro alkanes
	1120.5	C=C=C stretch	Allenes
	1314	CH 2 in-plane deformation	Trans two alkyl vinyl
	1637	C=N stretch	Hydrazone (solid)
	1964	C=C=C stretch	Allenes

ACKNOWLEDGEMENTS

This project is supported by the National Natural Science Foundation of China (61178036 , 31160250), Financial support was provided by the National High Technology Research and Development Program of China (863program) (no. 2012aa101906), Ganpo excellence project 555 Talent Plan of Jiangxi Province (2011-64), Center of Photoelctric Detection Technology Engineering of Jiangxi Province (2012-155),Graduate Science and Technology Innovation Fund projects of Jiangxi Province(YC 2013-S157).


Xue Xia

Yun Qiu

Lin Hu

Jingchao Fan

Xiuming Guo

Guomin Zhou

Cloud-basedVideo MonitoringSystemApplied inControl of Diseases and Pestsin Orchards

Keywords: video cloud, video monitoring, diseases and pests, apple orchards

As the proposition of the 'Internet plus' concept and speedyprogress of new media technology, traditional business have been increasingly sharedin thedevelopment fruits of the infomatization and the networking. Proceeding from the real plant protection demands, the construction of a cloud-based video monitoring system that surveillances diseases and pests in apple orchards has been discussed, aiming to solve the lack of timeliness and comprehensiveness in the control of diseases and pests in apple orchards. The system can not only monitor the growth state of all apple trees in orchards, but also detect apple trees' diseases and pests. The system featured a camera located under tree canopies that could precisely detect diseases and pests on the back of the leaves. On the cloud storage service side, plant protectors can get hold of the situation of apple trees' diseases and pests by using the software installed in a smartphone or a computer. The system provided pinpointsurveillance data and determining criterions for the control of diseases and pests of fruit trees, which had a great realistic meaning to the development of agricultural and ruralinfomatization.

Introduction

For a long time, traditional method for diseases and pests surveillance in orchards is going to the orchardsforchecking. Thiskind of monitoring method exist some drawbacks, such as the surveyedorchardis far from the plant protection station, uncertainly survey schedule and too much manpower input. On the other hand, during apple trees' growth period, some diseases and pests would occurred on the surface of the trees'leaves, but others would occurred on the back of the leaves. Orchard surveillance are typically monitored from top to bottom [START_REF] Mingmin | Analysis of the application on cloud computing platform-basednetworking videotechnology[END_REF], which lack the function of capture the video of the back of leaves. Then, the state and severity of diseases and pests of the back of leaves cannot be detected automatically. With the introduction of the 'Internet plus'concept and rapid development of new media technology, more and more traditional industries have been sharing the development fruits of the infomatization and the networking. Liu et al. combined cloud computing and environmental protection industryand discussed the application of cloud computing in the video monitoring of massive pollution sources [START_REF] Jingjing | Cloud computing-based application on the HD video monitoring of the massive pollution sources[END_REF]. In the field of food safetyretracing, using the Hadoop file system, Du studied its distributed processing architecture, data storage method and operation procedure of data read and write, which provided a new solution for the application of cloud video information processing in food safety tracing [START_REF] Ang | Application of Information Processing Technologyfor Cloud Video Monitoring in Food Traceability[END_REF]. According to the practical demand of agro-technology extension, Luan et al.discussed the cloud video service applied in visual agro-technology extension, and constructed a platform system of visual video service of agro-technology extension by using the spatial embedding technology of cloud video service [START_REF] Yina | The Cloud VideoServices Applied Research of Visualization Agricultural Science and Technology[END_REF][START_REF] Yina | Farm-oriented Requirements Analysis of Online Video Platform[END_REF]. Cloud computing is a fresh service that traditional computer technology and networking technologyintermingle. A new service pattern would be built by mobilized present information resources to satisfied different levels of users'needs of information service,which have characteristics of macro scale, virtualization and extendibility. As an inseparable part of the application of Internet, thedevelopment of cloud computing has become more and more extensive. Cloud computing adopt software as a service (SaaS) as its type of service mode. This service mode is a wholly novel software application mode that is gradually formed along with the continuous development of the Internet technology. This is just akind of extension to the host-client pattern [START_REF] Jiashili | Application of Cloud Computing in TV News Live Broadcasting Technology[END_REF].Emergence of the cloud computing promoted video monitoring to further development, and gradually formed a brand new mode of cloud service--video surveillance as a service [START_REF] Xiongyonghua | Research of energy consumption optimization methods for cloud video surveillance system[END_REF]. The mode and structure provided users a kind of video surveillance service, on which users just need concern about the demands of video preview and playback they wanted, and they do not care which devices run the service. This kind of fully virtualized method simplified its application steps, and enhanced its convenience. At present, the application of cloud-based video monitoring focus more on video meeting and Internet protocol television, but there is lack of application in the control oforchard's diseases and pests. In time surveillance to detect disease and pest is very important for fruit tree production, even directly influences the year-round fruit yield and quality. Yet, it is hard for traditionally manual operation to surveillance trees' diseases and pests in real time and Omnibearing.Therefore, a convenient monitoring solution is needed to deal with this issue. The video monitoring of diseases and pests that combined agricultural protection with video technology, networking technology and cloud computing technology is the concrete reflection of 'Internet plus' thinking on agricultural protection surveillance. Proceeding from the needs of the practical plant protection,the construction of cloud-basedvideo monitoring system that surveillance diseases and pests in apple orchards has been discussed.The system can not only monitor the overall growth state of apple trees in orchards, but also detect apple trees' diseases and pests. In particular, the system also can detect the diseases and pests hidden in the back of leaves. Surveillance diseases and pests by means of the spatial convenience of the cloud structure might save much time that plant protectors commuted between orchard in rural area and plant protection station in urban area , and provided more timely monitoring data more accurate distinguishing basis.

2Overall Frame Structure of the System

The system of video monitoring based on cloud computing is called the "cloud-based video monitoring system" [START_REF] Yih-Sing | The cloud streaming service migration in cloud video storage system[END_REF][START_REF] Vetro | Video transcoding architectures and techniques: an overview[END_REF]. The structure of cloud computing service mode allows cloud video monitoring nodes placed somewhere they want monitored. And then, the monitoring video stream would be accessed to the surveillance service center located on the cloud-side, andstored and managed uniformly [START_REF] Xiongyonghua | Design and implementation of a prototype cloud vid eo surveillance system[J][END_REF][START_REF] Qi | Design and implementation of virtualization-based middleware for clo ud simulation platform[END_REF].Users can access to the cloud-side via the Internet to check the real time monitoring video at any time. Cloud-basedvideo monitoring system of orchards'diseases and pestsdivide into four parts: acquisition side, gathering side, cloud storage service side and application side.Acquisition side is the equipment captured video data in orchards. It mainly includes cameras, transmission lines and control devices. Gathering side is the specialized digital video recorder which fuses the functions of Internet transmission, matrix display and encoding.Both of sidecooperate with each other and fulfilled the tasks of video acquisition, data encoding and data transfer.Cloud storage service side is animportantparts of the system that running data request from the acquisition side and the application side [START_REF] Xuguofan | Cloud-based video monitoring is running fromtheory to practice[END_REF].Besides, it also implements the functions ofsaving and managing a lot of monitoring video data of diseases and pests. Application side is belonging to the final client. Users issue a surveillance request to thecloud storage service side used by the desktop computer, notebook computer or smartphone to check the video of diseases and pests at a remote place. Sketch map of the overall system structure is shown in Fig. 1.

The Module of AcquisitionSide

The acquisitionside mainly consists of high definition dome camera, hemispheric camera and some auxiliary equipment. According to the requirements of modern management style of fruit trees, in full fruit period, height of fruit trees often remain under about 3m. And the height of surveillance pole need higher than trees' height in order to observe the entire orchard. So the height of surveillance pole designs with about 3.5m.Surveillance pole adopts stainless steel material quality that not only ensured stiffness of bracket, but also prevents rain erosion and field weathering. Cameras are fitted on the top and the middle of the surveillance pole. In order to observe the diseases and pests of the entire orchard, the top camera use a unibody waterproof dome camera that equipped with a 27-power binocular lens. Lower-middle part of the surveillance pole, under the canopy of the fruit trees, uses a hemispheric camera thatequipped with a 10-power binocular lens. Camera stretched into the tree canopy by means of the transverse branch arm to observe the occurrence status of diseases and pests. Similarly, taking rainfall issue into consideration, the sealed rainproof is installed on the external of the hemispheric camera. Physical picture of the surveillance pole is shown in Fig. 2. Power supply and control of the outdoor facilities use 4 cores copper wire, and divided the copper wire into two sets. One set is used as alternating current power supply. Anther set is used as signal line of PTZ control. Given that orchardists would regularly working in the orchard such as blossom and fruit thinning, fertilization and chemical spraying, electric shock are most likely to be occurred if 220V alternating current directly accessed into orchard. Yet the output stability of operating voltage cannot be ensured if transmit electric power with low voltage. Thus, the wire would firstly pass through in 5m high when routing and access to the 5m height relay pole. Andthen, the wire would access to the surveillance pole after reduced voltage used by an electrical transformer, which can cut down the probability of risk. Video signal of the orchard transferred by coaxial-cable (Syv75-5, theoretical distance is 300m). During the signal transmission, the signal strength would gradually attenuate. So the video signal-amplifier need appended to ensure the quality of the video transmission when the transmission distance is too long. Sketch map of wire arrangement in apple orchard is show in Fig. 3.

The Module of Gathering Side

The existing patterns of the orchard remote video monitoring most adopt wireless transmission. Although we do not have to consider much about wiring procedure, the steep drop of the video quality caused by the bottleneck of the wireless network bandwidth have limited the application of this technology in remote video monitoring, and it is hard to be practically promoted in remote video monitoring filed. With the gradually optimizing of the base installation, electric supply and web network have beenestablished in modern orchards. The gathering side of the orchard deployed a digital video recorder with the network transmission function. This side is usetoconverge the video signal from the surveillance pole, and output and display on the video matrix after converted the signal into image information. Meanwhile, by means of p2p network technology, the digitized video signal would access to the web via ADSL or optical fiber network and transferred to the server farm of the cloud. Users can penetrate the router and firewall through the p2p network technology to visit all of digital video recorder equipment in the cloud and capture the monitoring video of the tree's diseases and pests.In addition, the system would meet the downloading and accessing requirements of p2p pattern by lower the data storage granularity, which rendered sharing and interaction of video monitoring more immediate and simple, and avoided network congestionto some extent. The equipment drawing of the gathering side is show in Fig. 4.

The Module of CloudStorage Service Side

The cloud storage service side mainly consists of virtual machine,control servers and data storage servers. It can store the video data of diseases and pests from the digital video recorder equipment. The servers on the cloud can distinguish the video origin in the light of the particular ID heading code of the digital video recorder equipment, and saving the digitized video data into the databases of the server farms on the cloud side. Unlike the other platform of video monitoring, the cloud-based monitoring video storage server sides for orchards' diseases and pests not only need to completed the access and manage for the surveillance and browsing nodes by using the control servers, but also need to completed the scheduling and operating tasks from the acquisition side and gathering side. And finally achieved the functions in terms of the data save, manageand visit for the surveillance of diseases and pests.Structuremap of the storage service cloud sideis show in Fig. 5. Compute nodes (physical node) is the major carrier of virtual machine. The compute nodes of the storage service cloud side contains a lot of host machines and physical servers that processed the virtual machines of monitoring and browsing of orchard's diseases and pestsvideo. Every virtual machine can execute multiple monitoring or browsing tasks in order to accommodate frequent operations of tasks access and tasks shift out. The storage service cloud that based on virtualization technology can not only unified manages and distributes the video source, but also shield the physical position between storage entities and its particular multi-institutional property.The major duty of the control servers is assign permissions to the user in line with the service they requests, and providing different services for the different users. Meanwhile, it can also complete the functions of network access and user authentication [START_REF] Kun | Research on Cloud Data Storage Based on Hadoop and Its Implementation[END_REF]. The underlying data center is the base of distributed storage of monitoring video in cloud structure environment. It consists of servers, disk array and network communication control facilities. In which the storage servers is the most part of the underlying data center, and it responsible for the storage and management of video stream that data volume and growth rate are rather large. The technology of sliced file segmentation storage is adopted on the storage servers. Making the best of the I/O ability of every memory cells and optimizing storage methods, the large video files aredistributed stored in the physical storage facilities, which proposed to enhance the data rate of large video files between write and read, and lift the efficiency of storage and access of video data [START_REF] Yutang | Cloud technology and its application on videostorage[END_REF].

The Module of ApplicationSide

Users on the application side can acquire the monitoring video data of the assigned orchard from the cloud server farm by committing video request, which achieve the distributed remote surveillance of orchards' diseases and pests. The software client installed in the users' computers or smartphones, and they can access to the cloud server farm by means of inputting the ID heading code of the digital video recorder equipment. Through the high definition transmission with low bit-rate video, users on the application side can not only capture the clear real-time videoof the orchards' diseases and pests, but also preview and playback the video by means of the web storage function of cloud server farm so that users can get hold of the occurrence status of orchards' diseases and pests. The interface of video monitoring on application side is show in Fig. 6 Fig. 6The interface ofvideo monitoring on application-side

Results and Analysis

Regard to the ordinary video monitoring, the sheer amount of image information is anevident characteristic, so the big enough memory space is required. At present, the domestic video monitoring recorder equipment on the market mostlyequip with 1TB hard disk. If store video followed 720p format that is the rudimentary HD format, the hourly data size can reached 4G~8G per route. Even partial video compressing on the premise of remain video quality,the ordinary native video monitoring system required 17000G data size every month. Such a large video data is an insurmountable bottleneck for the native storage equipment's capacity, read/write performance and reliability in the mode of traditional video monitoring. By use of clustered application, grid technology and distributed file system, the cloud-based video monitoring system of orchard's diseases and pests interconnect and facilitate collaboration between memory service devices, providing the functions of monitoring data storage and visit, which achieved a fully virtualized procedure of video monitoring and decreased the costs of system constructionandapplication. The video monitoring cloud is fully transparent to users. They do not need regard the details of equipment's type, quantity and network structure, and just use anordinary web connection to access to the cloud side. In general, the mode of cloud-based video monitoring has break through the barriers of performance and storage capacity in traditional video storage. It enable users share the video resources in the monitoring cloud of orchard's diseases and pests, and through the clearly monitoring images, the occurrence of diseases and pests in orchard can be efficiently and conveniently estimate.

Conclusion and Prospects

With the continuous development of the video monitoring technology, more and more surveillance system calls for the achievement oflong-distance transmission of mass data. Yet the traditional surveillance patternis no longer applicable to current monitoring needs.The cloud-based video surveillance of orchard's diseases and pests is based on clouds, pipes and sides. Clouds refer tovideo monitoring service clouds.Pipes are the routes of data transmission. Sides are the user clients. In this structure, cloud may not just a complete one.Pipes probably have much more.The number of sides is countless [START_REF] Lijie | On the way of video cloud time[END_REF]. The video monitoring data of orchards' diseases and pests can intelligently shuttle and flow among clouds, pipes and sides in vertical and lateral styles. On the unceasingly development and progress of the cloud technology, the applications of video monitoring technologies in terms of cloud storing, cloud unicasting and cloud searching are bound to become mature. Cloud-based video monitoring pattern has become a trend in future surveillance, and the trend is certain to develop towards high definition, webified, intelligent and stereoscopic, which can provide further professionalsolution programs of orchards' diseases and pests for the field of agriculture and plant protection.

Fig. 1

 1 Fig. 1 Overall structure of the system 3Fundamental Function Modules 3.1 The Module of AcquisitionSide

Fig. 2

 2 Fig. 2 Physical picture of the surveillance pole

Fig. 3

 3 Fig. 3 Sketch map of wire arrangement in apple orchard

Fig. 4

 4 Fig. 4 Equipment picture of the convergence-side

Fig. 5

 5 Fig. 5 Structure map of the storage server cloud side

Acknowledgment

: This workwas supported by National High Technology Research and Development Program 863(2013AA102405), Agricultural Science and Technology Innovation Project of CAAS (CAAS-ASTIP-2015-AII-03).


Hao Xinning

Xie Nengfu

Zhong Sunwei

Xiaochun
email: azhongxiaochun@caas.cn

Zhang Xuefu

Research on Knowledge Base Construction of Agricultural Ontology based on HNC Theory

Keywords: agriculture ontology, HNC theory, knowledge base

With the development of agricultural research and production, agricultural information and data processing technology have become increasingly demanding. In recent years, organizing and expressing semantic knowledge based on the form of ontology has become the focus of research in artificial intelligence field. And the construction of ontology-based knowledge base is the basic condition. HNC theory has great advantages in processing of Chinese character. However, complexity of the system has restricted its development. This paper summarized the existing research results, took rice production (cultivation) and processing as an example, proposed an idea and methods for the knowledge base construction of agricultural ontology based on HNC theory.

INTRODUCTION

The rapid development of modern agriculture puts forward new and higher requirements for research on agricultural information. How to make a better use of modern information technology to innovate ideas and methods in the field of agricultural information science, and how to continuously promote research to enhance the performance of agricultural information have become an inescapable mission for research scientists in this area.

The rapid development of Internet, cloud computing, big data and other information technologies, and the continuous improvement of a variety of advanced mathematical theory spawn more IT research methods that are more convenient. Meanwhile, with the continuous advances in information science and technology, data management and information management have been difficult to fulfill modern demand for high-quality information. As a result, knowledge management has become one of the latest ways of information management. The main objective of knowledge management is to build domain knowledge in the form of knowledge networks, knowledge base and so on. This can reveal the relationship between semantic knowledge. Thus it is possible to form a more complete, proven and accurate knowledge platform and to support knowledge service, effective management and use of knowledge on the basis [START_REF] Zhou | A Review of Dynamic Knowledge Management Under Semantic Web[J[END_REF].

Nowadays, the mainstream technology of classification, retrieval, processing and filtering of agriculture information is still traditional key technology. However, this literal information retrieval system can handle only the literal meaning without carrying out more in-depth analysis, which leads to the lack of semantic understanding. Although it can make effective semantic analysis in artificial way to filter, process, classify and analyze information, the processing capacity and efficiency are insufficient to deal with the vast amounts of data generated daily. Therefore, only the thematic retrieval way, through the establishment of ontology knowledge base to describe relationship between domain concepts in an accurate, clear and standard way and use abundant examples to fully describe the conceptual system of domain knowledge, can effectively improve the matching degree of retrieving objects and then make the best use of agricultural information resources in a fast way to achieve the knowledge management of agricultural information.

Basic technical concepts

HNC Theory

Hierarchical Network of Concept (HNC) is a theoretical system for the entire natural language understanding and processing, which is founded by Professor Huang Zengyang, Institute of Acoustics.

The theory attributes semantic processing to conceptual representation. It uses primitive symbols and their combinations that highlight the relevance between concepts to represent conceptual semantic connotation. It represents the semantic content explicitly in natural language by mapping the natural language symbol system to HNC concept notation system. HNC concept notation system can describe lexical semantic, concept type and composition of the statement. The system can complete statement analysis of natural language through the concept analysis of statement and words [START_REF] Zengyang | HNC theory summary[END_REF]. HNC provides the basis for the computer to grasp the semantics by using HNC1, HNC2, HNC3 and HNC4 to digitize the word, sentence, sentence group and chapter. It contains not only the word level of knowledge, but also statement and context level of knowledge. The key point is to use HNC theory to process natural language and establish the mapping relationship between natural language and the concept space [START_REF] Zengyang | HNC(Hierarchical Network Concepts)theory[END_REF][START_REF] Shanshan | Retrieval of Periodical Literature Knowledge Element Based on Hierarchical Network of Concepts Theory[END_REF].

Ontology

Ontology is originated from philosophy area and it is a relative concept of Epistemology. Epistemology focuses on subjective perception, while ontology researches objective existence. So far, academic circles have not yet formed a unanimous conclusion on the definition of ontology.

Ontology uses its accurate, accepted concept definition to achieve specificity and semantic disambiguation of conceptual and terminology, and also completes the standardization and uniformity of reality concept definition. So it can guarantee the consistency between Human-Computer Interaction and machines and maximum the degree of realization of semantic disambiguation. The relationships between concepts, properties, instances and other components can achieve logical reasoning of knowledge concept as well as the description of knowledge conceptual system in the field. The relationships can effectively improve knowledge dissemination, sharing and retrieval efficiency in the aspects of semantics and pragmatics. It can also provide knowledge services under the big Data era background.

Ontology-based Knowledge Base

As a special form of knowledge, ontology aims to describe the facts and common vocabulary that are long-standing and unanimously approved by the knowledge workers. Knowledge base is aimed at describing the particular state of things that are related to facts and vocabulary, as well as the cognition state of knowledge workers. From another perspective, ontology refers to information that is unrelated to the particular state of things, while knowledge base refers to information that is related. From the point of structure to analyze, ontology provides a set of terms and concepts to describe a field, while knowledge base use these terms to express the fact in this area.

In natural language processing, semantic analysis requires the support of huge knowledge base. Since the 1980s, a number of semantic knowledge base have been developed in China and abroad, such as the famouse WordNet, HowNet, Beijing University of CCD, etc. All of these can be called ontology-based knowledge base from the perspective of knowledge representation.

Ontology mainly contains ontology concept layer and ontology instance level. Ontology Concept layer is a definition on the concept within a specific range and the relationship between them, which mainly aims to reveal the rich relationships between the concepts. Ontology instance level refers to instances corresponding to a concept. It is a concrete expression of certain concept. Numerous and full instances will greatly enhance the capacity and application value of ontology knowledge base. Ontology knowledge base can illustrate domain concepts and their relationships in an accurate, clear and standard way and fully describe the conceptual system of knowledge in the field. So it is good at semantic description, logical reasoning, and revealing the hidden relationship between the concepts. As a result, it can effectively solve the unhandled problems of information retrieval and information sharing [START_REF] Shouxue | Construction method of knowledge base based on ontology[END_REF].

Ontology-based knowledge base can not only reveal the framework of the domain knowledge, but also help domain ontology function in concept unification, standardization, knowledge retrieval, knowledge sharing and other applications. Unlike the traditional information retrieval techniques, Ontology-based knowledge base will accurately locate the required knowledge; profoundly reveal the meaning of semantic information. And with the continued expansion of the boundaries of the domain ontology, the boundary between some areas becomes fuzzy and interdisciplinary emerges. As a result, the scope of knowledge domain that ontology knowledge base covers will continue to extend to make it play a greater role. Paying more attention to information semantic meaning analysis will be the future trend of information retrieval and information world development of association data and Semantic Web.

Different from the symbolic representation of HNC theory, ontology-based knowledge base can clearly express hierarchy and relationship between concepts to facilitate people's understanding and application. Meanwhile, ontology using formal description language can also be directly applied to natural language processing. If the concept expression in HNC theory can be represented by the general form of ontology, the accuracy of the human-computer interaction can be effectively improved [START_REF] Jianyan | Construction and Application of Agricultural Word Knowledge base of HCN[END_REF].

Knowledge Base Construction of Agricultural Ontology based on HNC Theory

Ontology Construction Method

Ontology construction method mainly starts with the domain concepts and their relationship. With more suitable and mature ontology construction methods to guide the building process, we can effectively guarantee the consistency of domain ontology construction and also make ontology construction standardized and modularized. Duties, tasks and requirements in each part of the building process are clearly defined. There are no uniform, generally accepted principles of ontology construction [7-9]. In the ontology community, five principles proposed by Gruber are the most widespread:

(1) Clarity: Ontology provides concepts involved with clear, authoritative, accurate and standardized description. The definition of concept should be combined with specific areas and professional background. It should be objective, independent, authoritative and described formally with logical axiom.

(2) Consistency: Logic rules of the ontology should be strict and rigorous. The definition of concept inferred by logical axiom should be correct.

(3) Extensibility: The design of ontology should go with the changes and development in the field. And it can be adjusted and extended continuously.

(4) Smallest coding error: Conceptualization should specify the level of knowledge, not varying according to different symbolic coding.

(5) Minimum ontology commitment: The establishment of the ontology needs to satisfy specific knowledge sharing needs. If the coverage of the ontology is too large, it tends to lead to low specificity and ambiguous concepts, which will result in losing the characteristics of ontology itself.

Currently, the mainstreams of ontology construction method are as follows.

(1) TOVE: It is a relatively new method of ontology, which regards demand problems and Completeness Theorem as the considerations of ontology construction. However, it lacks documented process description and specific steps description of ontology construction.

(2) Skeletal Methodology: The method provides a framework and guidelines for construction in each stage, requires a documented process and gives the steps of ontology assessment. Therefore, it is full of reference value. However, it lacks specific methods and techniques, and only provides guidelines for the development of corporate ontology.

(3) METHONTOLOGY: The method provides a description of the steps of ontology assessment and is suitable for the development of large ontology programs. It first proposes the concept of "writing specification" and also details the ontology construction tools, concept sources and concept extraction method. This promotes the standardization and normalization of ontology construction. However, it doesn't provide any ontology assessment method, so it can't evaluate the quality of ontology construction.

(4) Cyclic Acquisition Process: The main contribution of it is a new method using a cyclic structure for ontology acquisition. However, it does not provide details of guidance and technical explanation for specific method of ontology acquisition. Therefore, it is difficult to put the method into practice.

(5) "Seven Steps" by Noy and McGuinness: Because of its comprehensiveness and systematisms, it is favored by many people, including us. Combined with Protege software, the main steps are as follows:

1) Determine the field and scope of ontology;

2) Consider reusing existing ontology;

3) List important terms in ontology; 4) Define the class and its hierarchy; 5) Define the property of the class; 6) Define the restrictions of properties; 7) Create an instance.

Knowledge Base Construction of Agricultural Ontology based on HNC Theory

Compare with traditional English words, agricultural vocabulary is significantly professional, it is necessary to establish a new vocabulary base. Taking rice as an example based on HNC theory, this paper establishes an agricultural ontology knowledge base and realizes the establishment, management and update of knowledge base by Protege software. Since the terms in the HNC Theory based agricultural ontology knowledge base are clearly defined, accurately expressed and unified in the conceptual level, it will not return duplicate or irrelevant results when retrieving. The architecture is shown in Figure 1.

Figure.2. HNC based ontology knowledge base model

The ontology knowledge base shows the semantic description of words in six fields: word form, concept category, HNC symbol, semantic category belonged, relevant semantic category and synonymous appellation. The features are as follows. First, the words included are different from common words when considering the word form. The words may contain the form of non-Chinese characters, such as numbers, letters, etc. Second, for the same thing, the corresponding appellation may be more than one. Third, it focuses on the deep semantic relationship between words.

A case study--rice production (cultivation) and processing

The construction of agricultural ontology knowledge base is a huge project. This research applied rice field as an example to briefly introduce the construction of ontology knowledge base. The overall framework is shown in Figure 3.

Figure.3. Overall framework of rice production (cultivation) and processing

The structure of ontology base contains 4 second-level directories and 10 third-level directories, Figure 4 is shown an example page of object properties in data processing, this experiment cumulatively includes more than 700 rice ontology terms, 108 object type attributes and 23 data type attributes to organize rice ontology knowledge base. In every single object properties, there are 4 different parts, every single part was to make a connection to an object property.

Figure.4. An example page of object properties in data processing

The final structure proposed rice production (cultivation) and processing ontology, including field cultivation techniques, rice harvesting, storage, processing and product, rice production machinery, rice varieties. The final result is shown in Figure 5.

Conclusion and Outlook

By combining HNC theory with knowledge base construction, this paper proposed the vision of knowledge base construction of agricultural ontology based on HNC Theory. Taking rice production (cultivation) and processing as an example, it initially realizes intelligent query of information to effectively improve the precision and recall. However, for lack of unified systematic classification standards in the field of agricultural production, future research should pay more attention to the systematicity and rationality of classification. The design of HNC codes is relatively simple and its relevance is not sufficient. This remains to be applied in further study.

Figure. 1 .

 1 Figure.1.The structure of ontology knowledge base

Figure. 5 .

 5 Figure.5. Rice ontology knowledge base

Acknowledgment

Funds for this research was provided by the CAAS Agricultural Science and Technology Innovation Program 2015


Bin Wang

Zixiao Chen

Jianmin Gao

Longsheng Fu

Baofeng Su

Yongjie Cui
email: bcuiyongjie@nwsuaf.edu.cn

Wang Bin

Chen Zixiao

Fu Gaojianmin

Su Longsheng

Cui Baofeng

Yongjie

PhD Cui Yongjie
email: cuiyongjie@nwsuaf.edu.cn

The Acquisition of Kiwifruit Feature Point Coordinates Based on the Spatial Coordinates of Image

Keywords: Kiwi fruit, harvesting robot, image coordinate, mathematical model, Kinect sensor

How to obtain the spatial coordinates of kiwi fruit has been one of the key techniques for kiwi fruit harvesting robot. In this paper, the writer proposes a unique way to obtain the spatial coordinates of the features of kiwi fruit from the bottom of the target fruit based on the growth characteristics and scaffolding cultivation pattern characteristicsof kiwi fruit, plus the help of Microsoft camera and Kinect sensor. Also included in this paper is the coordinate conversion between the images come from Microsoft camera and the images of the Kinect sensor, which is followed by an analysis of the precision of the spatial coordinates of Kiwi fruit captured by the Microsoft camera and Kinect sensor. The process is like this: first, capture images of the target fruit from the bottom of the fruit with Microsoft camera, and then extract coordinates of the target fruits' feature points to determine the corresponding target fruit feature point coordinates in the Kinect sensor; second, analyze the correspondence between the Microsoft camera image coordinate system and the Kinect sensor image coordinate system so as to establish amathematical model for the image coordinate conversion; finally,capture target feature points' spatial coordinates with Kinect sensorand conduct tests. The results show that the precision of coordinate conversion mode and Kiwifruit spatial coordinates can meet the requirements of the harvesting robots.

1 Introduction 1 The acreage and production of China's kiwi fruit rank first in the world. However, at present, the kiwi fruit is mainly harvested manually, which is highly labor-intensive. With the progress of urbanization and industrialization, more and more young and middle aged people are attracted to work in cities. As a result, the loss of labor force in agriculture is becoming serious, which in turn raises the cost of agricultural production and lowers market competitiveness of our agricultural products. Therefore, the development of kiwi fruit picking robot is of great significance to the development of China's kiwi fruit industry.

The key techniques of Kiwi picking robot involve three parts: fruit identification, location and nondestructive picking.

The widespread adoption of standardized scaffolding pattern in kiwi fruit production makes robot picking fruit feasible.

However, there are still several factors that hinders the development of kiwi fruit picking robot. Firstly, kiwi plants grow in clusters, each of which is usually composed of 3-5fruits, and the fruits usually grow too close to one another and even overlap. Moreover, foliage sheltering and similar color between fruits and the background make the harvesting robot difficult to perform precise fruit identification and separation as well as feature extraction of fruits. Secondly, kiwi fruit positioning and spatial coordinate acquisition are also problems to be solved for the development of the harvesting robot. The existing fruit and vegetable harvesting robot positioning system is low in precision, time-consuming, complex in structure and high in cost. So it is imperative to develop a new, efficient positioning system. Among the existing fruit and vegetable harvesting robots at home and abroad, some can harvest fruits whose colors are greatly different from the background colors, such as strawberry picking robot [START_REF] Kailiang | Design and Experiment of Elevated Substrate Culture Strawberry Picking Robot[END_REF], tomato picking robot [START_REF] Monta | Agricultural robot in grape production system[END_REF][3][START_REF] Kondo | Fruit harvesting robot in Japan [J][END_REF], citrus harvesting robots [START_REF] Caijianrong | Obstacle Identification of Citrus Harvesting Robot[END_REF][START_REF] Wei | Structural design and kinematics algorithm research for orange harvesting robot[END_REF], and some can harvest the target fruits whose colors are similar to the background colors, such as cucumber picking robots [START_REF] Van Henten | Tuijlet a1.An Autonomous Robot for Harvesting Cucumbers in Greenhouses[END_REF]. Such robots usually adopt near-infrared spectroscopy or laser technology for detection. In terms of detection and identification of kiwi fruits, Zhan Wentian [START_REF] Wentian | Recognition of kiwifruit in field based on Adaboost algorithm[END_REF]used Adaboot algorithm. Ding Yalan [START_REF] Yalan | Reacher on the Object Extraction of Kiwifruit Based on Images[END_REF] used RB color component method to separate kiwi fruits. These two methods can only separate the regions with fruits from the ones without fruits, but they failed to identify the fruit individually. Cui Yongjie [START_REF] Yongjie | A Method for Separation of Kiwifruit Adjacent Fruits Based on Hough Transformation[END_REF] used 0.9R-G color features in the fruit image segmentation, but in a complex background environment, this method involves a large amount of calculation and time. In terms of kiwi fruit positioning and coordinate acquisition, some other methods are used, such as monocular vision, binocular vision, multi-purpose vision, hyper-spectrum, laser etc. However, these methods have drawbacks, such as complex computation, low accuracy, high cost, poor reliability and so on. Meanwhile, the conversion among pixel coordinates, spatial coordinates and mechanical arms is still a problem to be solved.

In on-site investigation, it is found that the places below the fruits are spacious with less sheltering and the background is simple, so the writer proposes that the fruits be identified, positioned, and picked from the bottom parts of the plants. The principle is like this: determine the sequence of fruit identification, feature point extraction and fruit picking by using elliptic Hough conversion; acquire the feature point coordinates of the images with Kinect sensors made by Microsoft Company; obtain the image coordinates of feature points by Kinect sensor referring the foreign research results for Microsoft Kinect sensor in robot navigation [START_REF] Afiqahzainuddin | Autonomous Navigation of Mobile Robot Using Kinect Sensor[END_REF][START_REF] Ruiz | Development of a Control Platform for the Mobile Robot Roomba Using Ros and a Kinect Sensor[END_REF] and feature recognition [START_REF] Clark | Microsoft Kinect Sensor for Real-Time Color Tracking Robot[END_REF][START_REF] Dutta | Evaluation of the Kinect™ Sensor for 3-D Kinematic Measurement in the Workplace[END_REF][START_REF] Sgorbissa | Structure-Based Object Representation and Classification in Mobile Robotics through a Microsoft Kinect[END_REF]; finally, conduct coordinates conversion between the camera and sensors and construct mathematical model of the coordinates conversion to obtain the 3D coordinates of the feature points.

Information Perception

Feature Extraction and Image Acquisition

The kiwi fruit pictures were taken in October 2014 during harvest time at Kiwi Experimental Station of Northwest Agriculture and Forestry University and the breed is "Hayward". Camera used is Microsoft Life Camera studio with COMS sensor and auto-focus. Image pixel acquisition is of 640 × 360, jpg format. Each picture containing 2-5 fruitswas taken from the bottom with a distance of 20cm to the fruits. Image acquisition mainly comes from the side and bottom, as shown in Figure 1. In Figure 1, due to the greater scene depth, and complicated background, it can be seen that the images of fruits taken from the side contain not only the leaves of the near-byplant branches, but also distant non-target fruits, and serious mutual occlusion between the target fruits. All of these affect the accuracy of target fruit segmentation and recognition.

In contrast, the picture shot from the bottom has less mutual occlusion between fruits and no interference of other distant non-target fruits, which is favorable for extracting target fruits. Due to the mutual occlusion between fruits, fruit identification can only be performed from outside to inside, picking one by one, which results in low harvest efficiency.

In contrast, the shadow area between fruits is less in image shot from the bottom, which makes it possible that all the fruits can be identified at a time. As a result, the picking sequence can be determined and efficiency improved.

In order to improve fruit recognition success rate under complex background, Cui Yongjieet al [START_REF] Yongjie | Recognition and Feature Extraction of Kiwifruit in Natural Environment Based on Machine Vision[END_REF], researchers of Northwest Agriculture and Forestry University, presented a comprehensive method to identify fruits and extract fruit features according to kiwi fruit characteristics and color features, and elliptical Hough conversion. This method can minimize the impact of different complex background and illumination on the identification and the extraction of fruit features. The specific steps are shown in Figure 2.

2.2Picking Order

Figure 3 is the pixel coordinates of the feature points when each fruit is identified. X-axis is within the range of 0-360, Y axis 0-640.

Fig.3. Coordinates of target fruit

The picking sequence is determined according to the values of the feature point coordinates. In figure 3, 1,2,3,4,5 represent picking sequence, which is determined according to the Y coordinate values of the feature points from small to big, thus the picking arm of the robot reaches minimum stroke, and maximum efficiency in the whole picking process.

3Coordinate Conversion

The principle of Kiwi fruit extraction is shown in Fig 4 . On the left is a front view and on the right is atop view, where 1 stands for kiwi fruits, 2 for Microsoft camera, and 3 for Kinect sensors. The camera is used to identify fruits, extract features and determine the picking sequence. Kinect sensorsare used to obtain the spatial coordinates of the feature points of kiwi fruits. The intersection between the optical center and the outer surface of the infrared camera is used as the origin of the coordinates; the intersection between Microsoft camera lens surface and the optical center is used as the coordinate origin of its coordinate system as is shown in Figure 4 The relationship between Δx and Δy is positive and negative, and the image can be divided into our regions, that is, '1', '2', '3', '4'. WhenΔx < 0 and Δy < 0, it corresponds to region '1', representing that the feature point is on the upper left of the projection point. When Δx < 0and Δy > 0, it corresponds to region '2', showing that the feature point is on the lower left of the projection point. When Δx > 0and Δy > 0, it corresponds to region '3', meaning that the feature point is at the right bottom of the projection point; When Δx > 0 and Δy < 0, it corresponds to region '4', indicating that the feature point is on the upper right of the projection point.

When the distance between the camera and the feature point is h, letone of the image pixels be a (mm), then the three 3D coordinates Xw, Yw, Zw of the feature pointsrecognized relative to the origin are respectively as follows:

𝑋 𝑤 = 𝑎∆𝑥(3) 𝑌 𝑤 = 𝑎∆𝑦(4) 𝑍 𝑤 = ℎ(5)
In addition, the positive or negative values of 𝑋 𝑤 and 𝑌 𝑤 determines the regions where the feature points distribute.

The Kinect sensor is installedbelow the Microsoft camera with a fairly great distance between them, so that images acquired by the Kinect sensor include the shooting area of the Microsoft camera. As is shown in Figure 5 When the spatial position of the feature points recognized by the Microsoft camera remains unchanged, and let the coordinates of feature point A in the screen coordinate system of the Kinect sensor be x', y', then its relative pixel coordinates to the center point in the image are:

∆𝑥 ′ = 𝑥 ′ -320( 6) ∆𝑦′ = 𝑦′ -240 [START_REF] Van Henten | Tuijlet a1.An Autonomous Robot for Harvesting Cucumbers in Greenhouses[END_REF] Supposing one pixel of the image on plane H of the Kinect sensor represents the actual length b (mm), the 3Dcoordinates X 𝑘 ,𝑌 𝑘 ,𝑍 𝑘 of feature point A relative to the origin of the Kinect sensor are respectively:

𝑋 𝑘 = 𝑏∆𝑥′(8) 𝑌 𝑘 = 𝑏∆𝑦′(9)
𝑍 𝑘 = 𝐻 [START_REF] Yongjie | A Method for Separation of Kiwifruit Adjacent Fruits Based on Hough Transformation[END_REF] Similarly, the positive and negative of X 𝑘 , Y 𝑘 correspond to the locations of four images①②③④ and the actual locations of information. Since the spatial position of the Microsoft camera and Kinect sensors remain unchanged, pixel coordinates of feature point A in the imaged captured by Kinect sensor can be derived from the pixel coordinates of the feature point 'A' in the camera image shot by the Microsoft camera. That is 𝑎(x -320) + X = b(x ′ -320) [START_REF] Afiqahzainuddin | Autonomous Navigation of Mobile Robot Using Kinect Sensor[END_REF] 𝑎(y -180) + Y = b(y ′ -240) [START_REF] Ruiz | Development of a Control Platform for the Mobile Robot Roomba Using Ros and a Kinect Sensor[END_REF] From equations ( 11) and ( 12), we can get the following formulas: . When Microsoft camera recognizes and extracts pixel coordinates(x, y) of the feature point of kiwi fruits, the corresponding pixel on the Kinect sensor screen is(x', y')in theory. However, in practice, image acquired by the obtained by RGB video camera on the Kinect sensor is just opposite along the left-right direction. That is to say, it is reversed along the direction of axis X. In this case, the value of the coordinates (x, y) of the point corresponding to the coordinates (x'', y'') is (640 -X′,y′).

As far as the existing fruit and vegetable harvesting robots at home and abroad are concerned, a variety of methods are adopted for target positioning and coordinates extraction, such as monocular vision, binocular vision, multi-purpose visualization, close infrared spectroscopy, laser scanners etc. but each of them has some problems to be solved. In this study, Microsoft's Kinect sensor is used to obtain the spatial coordinates of the feature points of the fruits, and the development platform is Kinect for Windows SDK. The sensing device is shown in Figure 6 (a). It mainly consists of three parts. They are, from left to right, an infrared projector, a RGB camera, and an infrared camera. The function of the infrared projector is to project near-infrared spectrum actively. As is known, when the infrared spectrum is projected ontothe objects with rough surfaces or ground glass,there would be distorted spectrum, which in turn would will generate random points of reflected light (also called speckles). The speckles are then read by the infrared camera.The infrared camera is used to analyze the close infrared spectrum and to create depth images of the objects within our vision. RGB camera is used to shoot colored images within our vision. The measurement range is shown in Figure 6 (b).The range centers on the infrared camera with upper angle43° and lower angle 43°, 400-4000mm away in front of the video camera. The precision of the depth images captured within this area can reach millimeter. Kinect sensor was used to obtain the images of feature points, pixel coordinates and spatial coordinates. 4)Equations ( 13), [START_REF] Dutta | Evaluation of the Kinect™ Sensor for 3-D Kinematic Measurement in the Workplace[END_REF] were verified with the pixel coordinates acquired by Microsoft camera and Kinect sensor and the values of a, b.Errors in coordinate conversion were derived according to the actual pixel coordinates of the feature points while Kinect sensor acquiring images, D-values between the images coordinates derived from equations, D-values between pixel coordinates from Kinect sensor image and calculation, and the actual length represented by each D-value.

Result and Analysis

Through calibration, it is found that the actual length represented by one pixel 200mm away from at Microsoft camera is 0.445mm, that is to say, a=0.445m and that the actual length represented by one pixel 928mm away from the Kinect sensor is 1.32mm, that is to say, b=1.778. In this experiment,we got the pixel coordinates of 24 groups of feature points in different positions on the images captured by Microsoft camera and the images captured by Kinect sensor. In addition, with the help of Kinect sensor, we got the spatial coordinates of the feature points at such positions.

By plugging into formulas ( 13), ( 14 In the diagram, the red curves represent the actual distribution of points, and the blue ones represent the distribution of calculated points. It can be seen that the two curves coincide with each other. Table 1 indicates that the error between the calculated point coordinates and the actual coordinates is less than 3 pixels or 5mm, thus formulas ( 13), ( 14) can accurately reflect the correspondence of a same point on the image acquired by Microsoft camera and on the image acquired by the Kinect sensor.

Conclusions

(1)In light of the kiwi fruit growth characteristics, an automatic identification method is studied, including how to acquire fruit image from the bottom and an integrated application of fruit shape and color features in recognition.

(2)Considering the drawbacks of the existing fruit and vegetable harvest robots, a fresh method based on Kinect sensor is proposed to acquire the coordinates of the target kiwi fruits.

(3)This paper discusses the coordinate conversion between Microsoft camera and Kinect sensor and the mathematical model constructed can perform accurate coordinates conversion.

  (a) Picture shotfrom side(b)Picture shoot frombottom

Fig. 1 .

 1 Fig. 1.Two different shooting directions

Find

  Extract fruit boundaries by Canny operator Label local region boundaries Compute the minimal bounding rectangle Calculate the most elliptical shape by elliptical Hough transform threshold in 1.1R-G Eliminate noises by a morphological operation and an area thresholding method Is it a fruit boundary?

Fig. 2 .

 2 Fig.2.Flow chart of fruit recognition

  Fig.4.The Schematic of fruit space coordinates acquisition As is shown in Fig 4, the horizontal distance between the feature point and the Microsoft camera is 'h'; the distance between the feature point and Kinect sensor is 'H', then the distance between the Microsoft camera and Kinect sensor is 'H-h'. When the distance between the Microsoft camera and Kinect sensor remains unchanged, let the spatial coordinates between the camera and Kinect sensor be (X, Y, Z), then we get Figure 5.

Fig. 5 .

 5 Fig.5.Relationship between two coordinate systemsThe coordinate system diagram of Microsoft camera image is shown in Figure5(a) with a pixel area of 640×360. The value of the center coordinates is (320,180), which is also the projection position of the camera's optical center in the image. Letthe pixel coordinates of the feature point A recognized by the camera at the image be (x, y), so the value of the relative pixel coordinates in the image is (Δx, Δy). Then: ∆x = x -320(1) ∆y = y -180[START_REF] Monta | Agricultural robot in grape production system[END_REF]

  (b), 'XOY' is the Kinect image screen coordinates, and ′xoy′ is the Microsoft camera image screen coordinates. When the pixel range of the Kinect sensor in capturing image is 640 × 480, the projection of the optical center of the infrared video camera in the image is the pixel coordinates (320 × 240) of the center point of image projected.

  When the values of distance H and h remain unchanged, the values of X, Y, a and b would be constant, so would be

Fig. 6 .

 6 Fig.6.Hardware components and field of view of the Kinect sensor

Fig. 7 .

 7 Fig.7.Flow chart of spatial coordinates acquisition

Fig. 8 .

 8 Fig.8. Test equipment and images acquired 1)In order to facilitate verification testing, the whole coordinate conversion system was inverted: Kiwi fruit was placed at the bottom, and Microsoft camera was placed at the upper part with the Kinect sensor on top. The testing platform constructed is shown in Figure 8 (a).Kinect sensor and Microsoft camera are fixed onto the bracket on the same level with desktop supporting Kiwi fruit. In this test, in order to facilitate verification, the kiwi fruit surface was randomly marked with a point as the recognition feature point. Figure 8 (b) is an image acquired by the sensor and Figure 8 (c) is the image captured by Microsoft camera. 2) In this test, the vertical distance from the Microsoft camera to the feature point is 200mm, and the vertical distance from the Kinect sensor to the feature point is 928mm. Graph paper was used to calibrate the actual length between the image plane and the place 200mm away from the Microsoft camera as well as the actual length of one pixel 928mm away from the Kinect sensor (i.e. the values of a and b). The way of calibration is shown in Figure 9, where Microsoft camera is fastened to the height gauge parallel to the coordinate plane. Kinect sensor calibration method is same with Microsoft camera calibration method.

Fig. 9 .

 9 Fig.9.Calibration correspondence between the pixel value and the actual distance 3)Microsoft camera was used to obtain the images of feature points and the pixel coordinates of the feature point.

  ) the pixel coordinates of point 1 and point 24 on the images acquired by Microsoft camera and the symmetricalimage of the image acquired by Kinect sensor, we have the following results:The value of a/b coincides with the previous calibration. By plugging into formula (13), (14)the values of a/b, X/b, Y/b and the coordinate values of the rest 22 points on the images acquired by the Microsoft camera, we can work out the coordinates of these 22 points on the symmetrical images acquired of the Kinect sensor image.

Figure 13

 13 Figure 13 is a diagram drawn with MATLAB to express the calculated coordinate values and the actual coordinate values.

Fig. 10 .

 10 Fig.10. The calculated and actual coordinate distribution curves

Table 1 .

 1 The coordinates of points in different coordinate systems

		(x, y)	(x', y')	(x′′, y′′)	The coordinates after the	The spatial coordinates of the feature point
					formula extrapolated	coordinates obtained by Kinect(x, y, z)
	1	591,42	218,393	422,393	420,391	-214,-247,928
	2	594,154	218,423	422,423	421,420	-216,-301,928
	3	597,265	218,451	422,451	421,448	-213,-349,928
	4	597,324	218,467	422,467	421,462	-214,-375,926
	5	481,42	246,393	394,393	392,391	-166,-249,928
	6	482,151	246,421	394,421	393,419	-161,-294,928
	7	486,265	248,451	392,451	394,448	-158,-349,928
	8	484,322	248,465	392,465	393,462	-162,-375,928
	9	369,41	276,391	364,391	364,392	-112,-244,928
	10	372,153	276,421	364,421	365,420	-112,-299,928
	11	373,264	276,449	364,449	365,447	-112,-348,928
	12	373,319	276,464	364,464	365,461	-112,-371,926
	13	260,38	304,385	336,385	337,391	-62,-242,928
	14	260,153	304,419	336,419	337,420	-62.-294,928

261,266 304,449 336,449 337,448 -63,-344,928

Acknowledgment

This research was supported by grants from Natural Science Foundation of China (No.61175099) and Sci-Tech Co-Innovation engineering plan projects of Shaanxi Province (2015KTCQ02-12).