Wei Ma

Xiu Wang
email: bxiuwang@263.net

Lijun Qi
email: cqilijun@cau.edu.cn

Wei Zou

Development of variable rate system for disinfection based on injection technique

Keywords: Crop protection, Soil-borne disease, variable rate injection, control system

A variable soil pesticide injection system was developed for control of soil pesticide amount by PWM. The paper analyzed a algorithmic model of control system, and designed hardware, algorithm and control of soil pesticide, mainly software flow and a feedback control way. In the paper, the variable-rate control system was consisted of infrared sensor, speed sensor, PWM valve, and pump motor. According to the amount of soil pesticide information, controller can automatically control flow amount by adjusting solid solenoid valve and PWM valve based on working speed, which changes the pulse duty cycle to achieve the variable work. Injection experiments of soil pesticide was pre-set different dosage, the results shown that pesticide amount was precise in fact, and the errors was less than 3.2%. The system could achieve variable rate injection of liquid pesticide into deep soil based on infrared sensor. Fitting equation of flow amount by adjusting PWM valve based on working speed could draw the R2 value of 0.935.The chip can calculate the output PWM duty cycle according to the pre-set injection of soil pesticide amount after collected the speed of tractor. The feedback control is to regulate the PWM signal duty cycle according the real liquid flow obtained by the microcontroller chip which collected the output signal of liquid sensor which fixed on pesticide pipeline.

Introduction

In the suburbs of Beijing in China, greenhouses for agricultural production has developed very rapidly, mainly because of the rapid urban development and the population explosion in Beijing (Liyan [START_REF] Wang | The present situation and countermeasure development of greenhouse in China[END_REF]. Many farmers have planted with a variety of tomatoes for many years in the same piece of ground. And there are a lot of similar things. Such soil diseases cause huge losses to farmers, while an enormous impact on produce planting structure of the entire region.

Due to a large area of crops reduction caused by continuous soil diseases, farmers abandoned cultivation of eggplant.Farmers try to use pesticide on soil disinfection, taking examples as bellow: a. By the method of solution to fill the root with pesticide, but it will reduce the quality of vegetables; b. With the method of changing new soil, but it can lead to cost doubled. Precision agriculture, is the development trend of future agriculture, and a implementation of a set of modern farming operation technology and management mode, which is supported by the information technology according to the spatial variation, positioning, timing, quantitative(Min [START_REF] Yao | Development of precision agriculture and agricultural ecological environment protection in China[END_REF]. Precision agriculture technology has been used to solve farmers' problems in the greenhouse bases on the outskirts of Beijing. The technology, injecting pesticide directly, makes the storage of pesticide liquid be separated from water, the pesticide liquid, which is controlled by a peristaltic pump, flow into the main water pipe quantitatively to mix with water. It solved the issue of pesticide liquid treatment and equipment cleaning ( Landers,1992). Underground spay machinery need to solve the problem of soil environment. Pesticide air jet injector cultivated shovel is a kind of device to inject pesticide into the soil by high-speed air jet. No target spray causes pesticide deposition outside spraying target, it is one of the important reasons of low utilization rate of pesticide. While target spraying is a technology, which identify weeds by the use of near infrared light reflection, to spray selectively with targets through the control circuit and spray system (Cooke et al.,1996). It can save 60% ~ 80% of pesticide by using target spraying technology (Hanks et al.,2009). It is feasible to separate plants and soil by using camera sensitive to near infrared wave band when they studied the reflection characteristics of plant canopy and soil in visible light and near infrared band (Guyer, 1996).Fehon designed a kind of photoelectric sensor which identify soil and weeds by sensing reflectance on the weeds and background in the visible and infrared light section, its recognition rate of the soil and weeds is as high as 95% (Fehon et al., 1991).Deng Wei designed an infrared detection system targets based on infrared sensor technologies, different probe group with different coding modulation pulse infrared signal, can eliminate the interference of light paths between probe groups and other light signal (Deng Wei et al.,2008).Chen Zhigang measured and analyzed the different influence factors of infrared target detection effect in spray pesticide technology by actual plant, and indicated that plant appearance, light intensity, walking speed of detectors and plant spacing is relatively significant effect on the detection performance, while particularly the effects of plant shape and light intensity (Chen Zhigang et al., 2009). This paper studies the test of soil disinfection by injection technology to develop a variable control system to realize the precise control and variable regulation of soil disinfectant.

Experiments and Methods

Experimental Methods

The experiment was done in two parts according to the plant detection and precision dosage control. For the first experiment,six infrared sensors, 0.5m,E3F3-DS50N1(Yueqing city Gaode electric co., LTD,China), was used to get a infrared diffuse reflectance light feed with difference sensor fixed at different heights. Delayed response time is less than 2.5ms. The part used a group spectral sensors which is to detect target crops. Six sensors was fixed to both front sides of the tractor. The mounting height of the bottom sensor was set to be 220mm from the ground because the weeds height was not higher than 200mm.

The second part used PC software processing signals of infrared sensor for target detection and the speed sensor, and calculating the interval of acquisition time, obtaining the data of pesticide volume for different target root and sending it to the lower control computer. PWM was applied by the controller to regulate the dose. The controller calculated out the injection start and end time to control the injection volume. A control system for the PC control software was developed by using SamDraw3.3 development platform (Shenzhen display control automation technology co., LTD). PC software issue commands to control the duty cycle of spraying pump voltage after its calculation base on the data of distance from infrared sensor to the nozzle, the correlation coefficient between the output pulse of wheel speed sensor and the vehicle speed , the transfer function between the biomass detected by infrared sensors and the duty cycle of the solenoid valve on output pipeline, the base reference dosing per plant, the acquisition of biomass signals of the infrared sensor, speed pulse signal of the vehicle speed sensor and quantitative flow rate signal of the flow sensor. The flow chart of fixed-point pesticide injection expressed the logic of control program intuitively. The dose and the injection position is computed out quickly according to this process.

The test results were similar to Wang BingLin's results, after 30 days of inoculation, compared to normal eggplant plants, and plant height by infection of root knot nematodes decreased by 9.3%, stem and leaf fresh weight decreased 18.21%, and root vigor decreased 26.67%.A solution for injection into soil of root zone is demonstrated, which is used for crop infected by Meloidogyne detected by six sets of infrared sensor. The six sets of sensors detected targets independently and obtained corresponding scanning signal to each layer separately and sent the signal to the controller. As per circuit theory to analysis, Due to the response time is less than 2.5 ms for sensor to detect the target and the computing time is less than 1 ms for single-chip, the reaction time was very fast. So only one sensor could be used to detect at the same time, interfere with each other could be avoided completely in the actual test with multiple sensors used. Soil variable injection system included disc, plow and injector, and traveled by traction in the field.

The outline of the soil variable injection system could be figured out. In the test, walking speed was 1 m / s, the degree of fit of the actual target signal was not less than 95% after the signal be treated by controller model, and the relative error was not more than 3.2% for dosing adjustment by the controller. It is linear relationship between the duty cycle for flow control and per unit of time, the linear regression equation was y = 12.163x +7.6, where R2 = 0.935. The R 2 gives how much variance is explained by the model in term of overall variance in data. The R 2 is relative measure and is very intuitive. Fitting curve equation of duty ratio and flow linear relationship is y=12.163x+7.6,of which R 2 =0.935 , R 2 for the quadratic fitting curve equation is 0.9714(Fig. 4).

In order to test the precision of the dosing quantitative adjustment module quantitative, the pc controller software sent commands to the lower computer, the lower computer control dose automatically after receiving commands. The test data about the liquid quantitative adjustment are shown as in The problem was to be solved in two steps, the first step was to detect target and obtain the height

Conclusion

From measuring data, a linear relationship could be seen between the diseased plant height and root growth range. The relationship between eggplant height and root could be obtained by measuring the root excavated. And it is linear for eggplant with the same species and planted at the same time.

The relationship between duty and amount of pesticide is similar to linear approximation in experiment.The proposed approach is meant to be used in mathematical model for computing the relative time according to benchmark time point.

Fig. 1 .

 1 Fig. 1. The photograph of real objectThe test land was being used for eggplant planting from 2011.Using Wang Binglin method to measure the height, fresh weight (FW) and root size of eggplant, the root activity was determined by TTC method. 20 samples were collected on March 31th, 2014, and of which four are infested plant samples by root-knot nematode. The roots of normal plants and diseased plants were measured after excavation. The photograph of real object as shown in Figure1. There is visible difference in height and root between the normal and diseased eggplant plant. The left side in picture a is the healthy plants excavated, with large distribution root and high plant. The right side of in picture a is the plants infested by Meloidogyne, with small roots and low plant. It is easy to distinguish due to obvious difference respectively. Picture b is a comparison of roots. It is need to increase the dose for roots will stop growing after illness. Figure c is disease roots measuring. Figure d is normal roots measuring.

Fig. 2 .

 2 Fig. 2. infrared sensor for target detection As shown in Figure 2. The controller of fixed-point pesticide injection acquires data of target plant and adjusts different volume of pesticide for different target root injected into soil.

Fig. 3 .

 3 Fig.3. The flow chart of software

Fig. 5 .

 5 Fig. 5. The relationship between eggplant height and root

  information and position of the branches and leafs. Wherein the height of which was used to determine the prevalence of root crops and to calculate the pesticide amount need to be injected, and the position of the branches and leafs was used to build the computation model of time tree and calculate the injection width of the root and the relative benchmark time point. The second step was dynamic online adjustment of the flow and dose. Wherein the flow was calculated by controller according to the linear relationship to duty cycle, and sent commands to lower computer to realize. And the dose was obtained from the corresponding number of pulse to certain flow volume fed back by flow pulse sensor. Soil variable injection system was traction to travel in the field.

Table 2

 2 In order to test the target crop model, time tree was set up. The detect time of start T i1 and end T i2 for each six layers, the root zone injection time of start T s and end T t , and intuitive analysis of location and width of the injection zone. If the crop is tilted, the exact injection area of the root zone should be obtained relying on the model.Prediction of relative position of injection range for soil root zone is a very difficult thing. It is a certain uncertainty that the disease severity, of the underground root invaded by root knot nematode, was predicted according to the growth vigor of the target eggplant.

	width
	Distribution	value of root y/m
		Distribution width value of root x/m
		with 3.2% of maximum error of quantitative adjustment,
	0.4MPa of the maximum system pressure, 3.07% of error after correction. Duty ratio and flow
	have a good linear relationship. Fitting curve equation of duty ratio and flow linear
	relationship is y=12.163x+7.6.

Table 1 .

 1 At the pressure of 0.1MPa, the controller controlled the injection quantity automatically according to the calculated value by PC software.

				Volume of liquid /(ml)		Relative
	Set press. /MPa	Set volume	Test value	Test value 2	Test value	Average value/ml	error /%
		/ml	1/ml	/ml	3/ml
	0.1	50	52.3	50.6	51.9	51.6	3.20%
	0.1	100	107.2	104.5	102.7	104.8	2.47%
	0.1	150	151.3	152.4	153.5	152.4	1.60%
	0.1	200	202	200.3	205.2	202.5	1.25%
	0.1	250	255	250.9	253.6	253.2	1.27%
	0.4	250	239.3	245.8	241.9	242.3	-3.07%

Acknowledgment

This research was financially supported by the National High Technology Research and Development Program of China (Project Number: 2013AA102406) and Youth Fund Project(QNJJ201525) and Science and technology innovation team Project(JNKST201619) of Beijing Academy of Agriculture and Forestry Sciences. The authors would like to thank to engineer Liang Xiaofei and my assistant Liu Chuan for spending time acquiring experimental target data.


Cong Wang
email: wangcong@nercita.org.cn

Tianen Chen

Jing Dong
email: cdongj@nercita.org.cn

Shuwen Jiang
email: djiangsw@nercita.org.cn

Chao Li

Study of Spatio-temporal Variation of Soil Nutrients in Paddy Rice Planting Farm

Keywords: Spatio-temporal variation, Soil nutrition, Geostatistical analysis, Semivariogram model

It is significant to analyze the spatial and temporal variation of soil nutrients for precision agriculture especially in large-scale farms. For the data size of testing results is growing every time after sampling mostly by the frequency of once a year or several months, in order to discover the variation trends of specific nutrient which would be instructive for the fertilization in the future. In this study, theories of GIS and geostatistics were used to characterize the spatial and temporal variability of soil nutrients in paddy rice fields in the Erdaohe farm of Heilongjiang Province, China, which located in the north of Daxing'an Mountains, has an area of nearly 36.1 million hectares for paddy rice planting. The soil samples, collected from 2009 to 2013 once a year, were sampled based on the spatial distribution of paddy rice fields, counting as 651 in 2009, 1488 in 2010, 954 in 2011, 483 in 2012, and 471 in 2013. These samples were analyzed for pH, soil organic matter (SOM), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). In this study, we calculated and compared the spatial and temporal variation in whole farm area, using methods of exploratory statistical and geostatistical analysis. Conclusion acquired is that from 2009 to 2013, the spatio-temporal variations decreased in soil pH, AN, AP, AK, and increased in SOM. Moreover, according to the comparison of interpolation results, these five soil properties in Erdaohe farm remained not very stable in the past five years, which could implicate important significance in future research for consideration of correlation amongst fertilization, rice yield and other factors especially in large-scale farms.

Introduction

Soil nutrients provide a scientific accordance in fertilizer applications, especially in paddy rice planting farms. However, soil properties not only have spatial variability, but also oscillate with the time changing. It's meaningful to analyze the extent of temporal and spatial variation of soil nutrient contents for more reasonable fertilizer applications. Lots of works have been actualized on soils by measuring and analyzing the spatial dependencies on soil fertility [START_REF] Tesfahunegn | Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia[END_REF][START_REF] Sun | Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China[J][END_REF][START_REF] Bogunovic | Spatial variation of soil nutrients on sandy-loam soil[END_REF][START_REF] Ferreira | Spatial variability of soil properties and soil erodibility in the Alqueva reservoir watershed[END_REF]. For example, Weijun Fu et al. studied the spatial variation of soil nutrients in a dairy farm in southeastern Ireland [START_REF] Fu | Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application[END_REF], Kelin Hu et al. studied patterns of spatial and temporal variation of SOM in Beijing's urban-rural transition zone [START_REF] Hu | Spatial and temporal patterns of soil organic matter in the urban-rural transition zone of Beijing[END_REF], and Zhang Xing-Yi studied the spatial variability of nutrient contents in northeast China where has black soils [START_REF] Zhang | Spatial Variability of Nutrient Properties in Black Soil of Northeast China[END_REF]. However, most of the researches learned temporal variation in period of time with long intervals, neglecting the continuous changing year after year [START_REF] Huang | Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices[END_REF]. In this paper, methods of statistics and geostatistics were applied to study the spatio-temporal variation for data of soil pH, available nitrogen (AN), soil organic matter (SOM), available potassium (AK), and available phosphorus (AP) collected from 2009 to 2013 once a year in a paddy rice planting farm in northeast China.

Materials and Methods

Study area

Fig.1. Location of the study area

The farm of Erdaohe located in northeast boundary of China, closed to Russia across the Ussuri River in the east, and the Heilongjiang in the north (Fig. 1). This area belongs to the Sanjiang Plain, and has a humid or semi-humid continental monsoon climate of the North Temperate Zone, which is suitable for agriculture production, especial for paddy rice planting. The total area of this farm is 534.2 kilometers, with an area of 362 kilometers for cultivated land, including 360.7 kilometers for paddy rice planting. The soil samples were planed to collect at the depths of 0-20cm from 2009 to 2013 once year (Fig. 2). The sample time were mostly between autumn harvests and fertilizers. Generally, this work was mostly done by experienced technicians, who has a quantity knowledge of agriculture production in the sampling region. The count of sampling points are 651 in 2009, 1488 in 2010, 954 in 2011, 483 in 2012, and 471 in 2013. On the other hand, location of these points were selected according to space distribution of land parcels, soil types, land use types and experience of technicians. After sampling, soil samples were naturally dried at ventilation place and then sieved to pass a 2-mm mesh after crushed. In this article, soil test results of pH, soil organic matter (SOM), available potassium (AK), available nitrogen (AN), and available phosphorus (AP) are used for spatio-temporal variation analysis, the soil test methods are revealed in Table 1.

Soil sampling

Exploratory statistical analysis

In this paper, methods of exploratory statistical and Geostatistical analysis are both chosen to study the temporal and spatial variation of soil nutrient contents in this paper's study area. First of all, descriptive analysis indexes such as maximum (max), minimum (min), median, mean, skewness and kurtosis, coefficient of variation (C.V.) and standard deviation (S.D.) were chosen to achieve the summary information of soil nutrients distribution. These indexes can be divided into three different types: location, spread, and shape, which provide diverse descriptions of soil nutrients. Index of mean is the arithmetic average of data measured, which shows the center of the distribution of the original data. Besides indexes described above, Normal Q-Q plots (quantile-quantile plots) were created to identifying the probability and some distinct outliers (same as extreme values).

Usually on the x-axis marked the observed values, and for a normal distribution values expected were marked on the y-axis. In general, samples which have a normal distribution cluster would follow a diagonal straight line [START_REF] Evans | Statistical distribution[END_REF]. Meanwhile, on the normal Q-Q plots, it's easy to observe the low or high value outliers, because these points will be away from the calculated normal Q-Q line.

Geostatistical analysis

In this study, the spatial variation of each soil nutrient content was measured by geostatistics. Experimental variogram evaluator is approximately uninfluenced for any inherent random function, whatever it's really sensitive to external values since it is on account of squared distinctions among calculated data. The semivariogram model was established for each microbiological parameter for the sake of characterizing the level of spatial variability between neighboring samples. Meanwhile, the proper model function was suitable to the semivariogram model. The value of semivariogram γ(h) was calculated using equation below

() 1 1 ( ) ( ( ) ( )) 2 ( ) Nh i i i h Z x Z x h Nh       (1)
In the equation, h represents the demarcation distance from the locations of x i to locations of x i +h. Z(x i ) and Z(x i +h) represents the values which are calculated for the regionalized variables at locations x i or x i +h. The last one N(h) represents the quantity of two sets at any demarcation distance of h [START_REF] Piotrowska | Spatio-temporal variability of microbial biomass content and activities related to some physicochemical properties of Luvisols[END_REF][START_REF] Burgess | Optimal interpolation and isarithmic mapping of soil properties[END_REF].

Including spherical, Gaussian, exponential, linear and power models, there are several models available to adjust the experimental semivariogram [START_REF] Liu | Spatio-temporal variations of soil nutrients influenced by an altered land tenure system in China[END_REF][START_REF] Wang | Geostatistics and Its Application in Ecology[END_REF][START_REF] Goovaerts | Geostatistics in soil science: state-of-the-art and perspectives[END_REF]. On the other hand, a semivariogram includes three primary parameters which define the spatial structure of original data as: γ(h)=C 0 +C. Where C 0 delegates the nugget effect which means the local variation coming at scales smaller than the samples' interval, like sampling error, measurement error and fine-scale spatial variability. The sum of C 0 and C is the sill which represents total variance in the equation. The distance is called the range at which the semivariogram levels away from the sill. Furthermore, sampling points are not spatially connected whenever the numerical value of separation distances is larger than the range [START_REF] Piotrowska | Spatio-temporal variability of microbial biomass content and activities related to some physicochemical properties of Luvisols[END_REF].

The equation of different models are described below. Model of spherical anisotropic was adjusted to the empirical semivariance, which is defined as:

3 0 1 0 1 3 ( ) ( / ) / 2 0 2 () (0) 0 0 h h C C h a h a a h C C h a h                             (2)
In the equation, C 0 represents the nugget value which means the spatial variability produced by the random components such as micro-scale processes and measured error. C 1 is the structural variance which means the spatial heterogeneity produced by spatial autocorrelation. C 1 + C 0 represents the sill, while A represents the range (or spatial correlation distance). Other stationary models such as Gaussian (Eq. ( 3)), exponential (Eq. ( 4)) and linear (Eq. ( 5)) equations are defined as:

2 0 1 ( ) 1 exp( / ) h C C h a          (3)   0 1 ( ) 1 exp( / ) h C C h a      (4) 0 () h C bh   
(5) Where C 0 , C 1 , h and a represent the same meanings as spherical anisotropic model, while b is slope of the semivariance line in Equation ( 5).

Results and Discussion

Variation of soil properties in past five years

According to exploratory statistical analysis, Table 2 shows the soil nutrients determined values of minimum, maximum, median, mean, coefficient of variation (C.V.), standard deviation (S.D.), kurtosis and skewness from 2009 to 2013. First of all, considering the values of different soil nutrients described by min, max, median and mean, in the past five years, test results of pH shows a relatively inflexible constant, while the other four properties appears more variable. Among these nutrient types, except for SOM, the variation degree tested by C.V. (%) all decreased with small fluctuations in the past five year. Furthermore, for pH data the C.V. value was relatively small while that for AK data was relatively large. On the other hand, the degree of dispersion tested by S.D. shows that the sequence from high to low is AK, AN, AP, SOM, AP and pH, besides, the degree of dispersion of SOM increased significantly in recent two years. At last, kurtosis and skewness indicated the shape of distribution of soil raw data compared with normal distribution. The results showed that except for AK and several years' data of SOM and AN, the others were closest to normal distribution. Transformation was needed for the followed analysis. With the combination of exploratory statistical analysis and normal QQ-plot analysis, different transformation method were chosen, seen from Table 3. Figure 4 shows the Normal QQ-plots of data before and after the transformation for AK values in 2011, it is obvious that after transformation, the points were more fitted with the straight line.

Spatio-temporal variation of different soil properties

According to the theory of geostatistical, semivariance analysis was applied to soil properties from 2009 to 2013, the results indicated that spatial autocorrelation existed for the soil properties in study area, which means spatial interpolation method of kriging could be used to predict the soil nutrients in missing data area. While the step was to choose the appropriate semivariogram model for each property and each year, Figure 5 shows the semivariogram for soil pH in 2009 (anisotropic) with different models (Spherical, Gaussian and Exponential).

Fig. 5. Semivariograms of different models for soil pH in 2009

In order to select the best model for following analysis, comparison of precision for different models is needed. [START_REF] Trangmar | Application of geostatistics to spatial studies of soil properties[END_REF]. This value was used to define different classes of spatial dependence for the soil variables as such rules: a. If the ratio was smaller than 25%, the variable was considered extremely spatially dependent; b. If the ratio was between 25% and 75 %, the variable was considered to be moderately spatially dependent; c. If the ratio was larger than 75%, the variable was considered to be weakly spatially dependent, which indicated that random factors were the majority factors affect the spatial variation of soil properties [START_REF] Cambardella | Field-Scale Variability of Soil Properties in Central Iowa Soils[END_REF]. From this table, it is clear that all of the soil properties measured in this study were not strongly spatially dependent. From 2009 to 2013, the value of Nugget/Still for each soil property decreased in overall trend, relatively, soil pH was the considered as the most strongly spatial dependent, while soil AN was the weakest one in study area.

According to the results below, interpolation method of ordinary kriging was used to predict the spatial distribution maps for soil nutrients in study area. Figure 6 shows the prediction maps for soil pH from 2009 to 2013. For the limitation of paper length, the maps of other soil nutrients were not shown in this paper. The spatial distribution maps of soil pH shows that the soil pH in this study area were mostly acidic, or strongly acidic in some regions. From 2009 to 2011, the spatial distribution is similar in the whole area, while there was obvious changes in 2012, especially in the south area, soil pH became more acidic. Until the next year, distribution changed similar as that in 2009. For other soil nutrients, the spatial distribution remained not very stable in the five years, which probably because of the fertilization changed every year.

Conclusions

This study analyzed the spatio-temporal variation for several soil properties in a paddy rice farm from 2009 to 2013. Among these soil properties, data of soil pH collected all the five years followed a normal distribution, and had a relatively small C.V. values which decreased along the study time range. On the other hand, the spatial variation of soil pH increased, became more strongly spatial dependent. The others, such as soil AK, followed a log-normal distribution in 2009, 2010 and 2012, while in 2011 and 2013 followed neither normal nor log-normal distribution. Thus, data transformations were acquired for better analysis. Except for soil pH, raw data of other soil properties were transformed by log or box-cox method more or less. According to the analysis results, the spatial variation of soil pH, AN, SOM, AP and AK all increased from 2009 to 2013, while except soil pH, most of that were not strongly spatial dependent, which means the spatial variation was mostly based on random factors in this study area.

Fig. 2 .

 2 Fig.2. The distribution of soil sampling points from 2009 to 2013

4. 2 Fig. 3

 23 Fig. 3 Normal QQ-plots of soil nutrients in 2009

Fig. 4

 4 Fig. 4 Normal QQ-plots of AK raw data and transformation result Box-Cox (λ= -0.4) in 2011

Fig. 6 .

 6 Fig. 6. Spatial distribution maps for soil pH (2009-2013)

Table

Table 2 .

 2 The statistical values of soil properties.

		Year min	max	mean	Median S.D.	C.V. (%)	Skewness	Kurtosis
		2009 4.60	6.40	5.50	5.60	0.26	4.77	-0.22	3.79
		2010 5.10	6.50	5.55	5.50	0.21	3.73	0.45	3.36
	pH	2011 4.98	6.40	5.57	5.58	0.20	3.60	0.20	3.17
		2012 4.88	6.52	5.52	5.52	0.24	4.42	0.52	4.64
		2013 4.94	6.21	5.54	5.54	0.17	3.11	-0.19	3.10
		2009 11.20	69.56	40.44	40.66	8.40	20.77 0.00	3.62
		2010 17.79	59.19	39.55	44.66	6.51	16.45 -0.18	2.69
	SOM	2011 12.50	74.20	39.25	39.08	8.69	22.13 0.34	3.83
		2012 23.20	154.00 43.11	41.80	10.56 24.49 3.37	31.05
		2013 12.60	379.80 43.29	41.00	24.43 56.44 10.67	143.39
		2009 111.20 507.50 231.85 229.30
	AN

Table 3 .

 3 Data transformation method selected

		2009	2010	2011	2012	2013
	pH	a	a	a	a	a
	SOM	a	a	a	b	c(λ= -0.

Table 4 .

 4 Table 4 offered the precision analysis results of spatial data of soil nutrients in 2009. For in condition that mean standardized closer to zero, the root-mean-square was smaller, average standard error closer to root-mean-square, and root-mean-square standardized closer to one, semivariogram model may be the most appropriate one. Based on the precision errors, the best semivariogram model for soil data collected 2009 were: Spherical for pH and SOM, Gaussian for AN, Exponential for AP and AK. Comparison of precision analysis among different models for soil test data in 2009 Table5displays the selected best models for each soil properties from 2009 to 2013 and their parameters. First of all, directional features were observed for the majority soil data except for SOM values collected in 2009 and 2010, which also became the special cases of range values above 25 km. The value of Nugget/Still shows the relative size of the nugget effect among varied soil properties

	g 10			g 10
	0.88			0.88
	0.66			0.66
	0.44			0.44
	0.22			0.22
	0			0
	0 -0.22	Distance, h 10 -4 0.34 0.67 1.01 1.34 1.68 2.02 2.35 2.69	0 -0.22	Distance, h 10 -4 0.34 0.67 1.01 1.34 1.68 2.02 2.35 2.69
		(a) Spherical			(b) Gaussian
		g 10
		0.88
		0.66
		0.44
		0.22
		0
		0 -0.22	0.34 0.67 1.01 1.34 1.68 2.02 2.35 2.69
			Distance, h 10 -4
			(c) Exponential

Table 5 .

 5 Semivariogram models selected for soil nutrients and parameters of each model

					(2009-2013)
		Year	Model	Anisot-ropic	Still	Major range(m)	Nugget	Direction	Nugget/ Still
		2009	Spherical	Yes	0.074	25577.3	0.058	277.6	0.79
		2010	Exponential	Yes	0.036	26993.5	0.019	39	0.35
	pH	2011	Exponential	Yes	74.571	25829.5	0.021	276.8	0.45
		2012	Exponential	Yes	42.344	26322.6	0.021	336.5	0.28
		2013	Exponential	Yes	76.740	25442.6	0.011	79.6	0.31
		2009	Spherical	No	0.0491	3584.73	59.571		0.80
		2010	Spherical	No	0.0045	4148.61	41.847		0.99
	SOM	2011	Exponential	Yes	0.141	26133.4	71.409	290.7	0.93
		2012	Spherical	Yes	3263.87	25427.1	0.035	312.7	0.72
		2013	Exponential	Yes	1799.66	25493.9	0.0025	56.4	0.56
		2009	Gaussian	Yes	0.0217	25507.6	0.126	316.3	0.90
		2010	Exponential	Yes	0.041	25902.3	3156.4	66	0.97
	AN	2011	Exponential	Yes	99.234	25766.3	1535.3	25.8	0.85
		2012	Exponential	Yes	97.084	25321.6	0.017	307.1	0.80
		2013	Exponential	Yes	88.399	26021.5	0.027	239.6	0.66
		2009	Exponential	Yes	289.06	25667.5	77.23	29.4	0.78
		2010	Exponential	Yes	768.09	26219	64.194	51	0.66
	AP	2011	Spherical	Yes	0.227	26795.5	47.283	48.8	0.53
		2012	Exponential	Yes	0.175	26322.6	250.5	59.175	0.87
		2013	Spherical	Yes	0.003	26021.5	345.93	61.6	0.45
		2009	Exponential	Yes	0.145	26556.1	0.169	240	0.75
		2010	Exponential	Yes	0.0009	26846.3	0.101	63.2	0.58
	AK	2011	Spherical	Yes	0.074	26086.2	0.0018	33.2	0.64
		2012	Spherical	Yes	0.036	25407.7	0.098	24.6	0.67
		2013	Exponential	Yes	74.571	25215.6	0.0007	46.4	0.75

Acknowledgment

Financial supports from the National Science and Technology Support Project of China (Grant No. 2013BAD15B05) and the Natural Science Foundation of Beijing, China (Grant No. 4151001) are sincerely acknowledged.


Yang Liu
email: luisyang@126.com

Chunlin Shi

Shouli Xuan

Xiufang Wei

Yongle Shi

Zongqiang Luo
email: fluozongqiang@163.com

Effects of Waterlogging and Shading at Jointing and Grainfilling Stages on Yield Components of Winter Wheat

Keywords: winter wheat, waterlogging, shading, yield components

Waterlogging and shading result from continuous rain are the main meteorological disasters for wheat (Triticum aestivum L.) production. In order to evaluate the effects of waterlogging and shading on yield components of winter wheat (both independent and combined), pot experiments were conducted using two representative cultivars in local, Ningmai 13 and Yangmai 13. In total, 4 treatments, including CK (control), WA (waterlogging alone), SA (shading alone) and WS (both waterlogging and shading) were established with three duration (5, 10 and 15 d, respectively) at jointing and grain-filling stages. Results showed that, in the case of non-stressed environment, Yangmai 13 got a better production compared with Ningmai 13 (grain yield per plant was 14.25 g and 15.97 g for Ningmai 13 and Yangmai 13, respectively). However, compared with Yangmai 13, Ningmai 13 got a better yield under stresses at jointing stage, while a similar yield was observed when stresses are at grain-filling stage. By comparing wheat yield and its components, the negative effects of the stresses showed a tendency that WA> WS> SA at jointing stage, whereas WS> WA> SA at grain-filling stage. The result demonstrated that shading had a compensative effect on waterlogging at jointing stage while an addictive effect at grain-filling stage. Reduction of wheat production caused by continuous rain depended on the growth stages. Effect of growth stage on grain yield should be considered when waterlogging and shading packages of wheat growth model were established.

Introduction

The winter wheat production in Jiangsu Province covers an area of 2.13 M ha, accounting for approximate 9% of the overall winter wheat area of China in 2012 [1] . However, most winter wheat in Jiangsu is planted in paddy fields in a rice-wheat rotation [START_REF] Li | Waterlogging pretreatment during vegetative growth improves tolerance to waterlogging after anthesis in wheat[END_REF] , which results in poor drainage conditions. Furthermore, continuous rain is frequent during the growth season of winter wheat due to the subtropical monsoon climate [START_REF] Shi Chunlin | A WCSODS-Based model for simulating wet damage for winter wheat in the middle and lower reaches of the Yangtze-River [J][END_REF][START_REF] Zhiqing | An early warning system to predict waterlogging injuries for winter wheat in the Yangtze-Huai Plain (WWWS) [J][END_REF] . The total rainfall is 500-800 mm during the wheat growth season, which far exceeds requirements for winter wheat production [START_REF] Jiang | Nitrogen fertiliser rate and post-anthesis waterlogging effects on carbohydrate and nitrogen dynamics in wheat[END_REF] . In addition, frequency of extreme weather events is increasing globally and regionally [START_REF] Zhiqing | An early warning system to predict waterlogging injuries for winter wheat in the Yangtze-Huai Plain (WWWS) [J][END_REF] . Therefore, soils are easily waterlogged in Jiangsu and waterlogging has become a prime limitation for wheat production locally.

Previous experimental results on plants have shown that waterlogging stress restricts root growth, decreases root hydraulic conductance [START_REF] Araki | Waterlogging at jointing and/or after anthesis in wheat induces early leaf senescence and impairs grain filling[END_REF] , results in leaf senescence [START_REF] Chunfang | Effects of salt and waterlogging stress at post-anthesis stage on wheat grain yield and quality [J][END_REF] , shortens the grain filling time [START_REF] Dickin | The effects of winter waterlogging and summer drought on the growth and yield of winter wheat (Triticum aestivum L.) [J][END_REF] and reduces final wheat yield [START_REF] Malik | Short-term waterlogging has long-term effects on the growth and physiology of wheat [J][END_REF][START_REF] Araki | Waterlogging and hypoxia have permanent effects on wheat root growth and respiration[END_REF] . The influence of waterlogging on yield components of wheat has been studied by many researchers. Grain yield under waterlogging treatments was 10-15% lower than that under non-waterlogging treatments [START_REF] Araki | Waterlogging at jointing and/or after anthesis in wheat induces early leaf senescence and impairs grain filling[END_REF] . Plot experiment showed that waterlogging from anthesis to maturity would accelerate the senescence of flag leaves, leading to a reduction of grain-filling rate and grain weight [START_REF] Xuemei | Effects of nitrogen supply on flag leaf senescence and grain weight in wheat grown under drought or waterlogging from anthesis to maturity [J][END_REF] . Results of a pot experiment indicated that waterlogging reduced the accumulation of starch in the grains, and the allocation of carbon assimilates to grain yield [START_REF] Chunfang | Effects of salt and waterlogging stress at post-anthesis stage on wheat grain yield and quality [J][END_REF] . However, damage from waterlogging depends on the growth stage, waterlogging duration and wheat cultivar [START_REF] Rasaei | Impacts of waterlogging on shoot apex development and recovery effects of nitrogen on grain yield of wheat [J][END_REF][START_REF] Hayashi | Maintained root length density contributes to the waterlogging tolerance in common wheat (Triticum aestivum L.) [J][END_REF] . Thus, different growth stages should be considered when establishing experiment.

Shading always accompanies waterlogging during continuous rain events. Similarly to waterlogging, shading could also reduce dry matter accumulation and grain yield [START_REF] Slafer | Preanthesis shading effects on the number of grains of three bread wheat cultivars of different potential number of grains[END_REF][START_REF] Abbate | Grain yield in wheat: effects of radiation during spike growth period[END_REF] by reducing radiation, impairing net photosynthesis in leaves [START_REF] Wang | Allocation of Photosynthates and Grain Growth of Two Wheat Cultivars with Different Potential Grain Growth in Response to Pre-and Post-anthesis Shading [J][END_REF] and reducing the LAI (leaf area index) [START_REF] Huan | Long-term Low Radiation Decreases Leaf Photosynthesis, Photochemical Efficiency and Grain Yield in Winter Wheat [J][END_REF] . However, diffuse light increase under shading can compensate for the reduced radiation [START_REF] Greenwald | The influence of aerosols on crop production: A study using the CERES crop model[END_REF] . The decrease of LAI is compensated by increases of the bottom leaf area, and the reduction in photosynthetic rate (Pn) of flag leaf is compensated by the increase in Pn of the third leaf [START_REF] Huan | Long-term Low Radiation Decreases Leaf Photosynthesis, Photochemical Efficiency and Grain Yield in Winter Wheat [J][END_REF] . Furthermore, shading increases transport of dry matter from organs to grain [START_REF] Huawei | Effects of shading on morphology, physiology and grain yield of winter wheat [J][END_REF] . Thus, the shading effect depends on the cultivar and the shading level applied [START_REF] Huawei | Effects of shading on morphology, physiology and grain yield of winter wheat [J][END_REF] .

Although the physiology of wheat under waterlogging and shading stress has been studied independently, research on the combined stress is still scant, especially in consideration of different stress durations and different growth stages. Hence, the objective of the present study was to study the independent effects of waterlogging, shading as well as their combined effect on the yield components of winter wheat at jointing and grain-filling stages. The results will advance our understanding of winter wheat yield under continuous rain events and could be used to improve wheat yield evaluation models by calibrating the combined effect of waterlogging and shading.

Experiments and Methods

Experimental design

The experiment was established during the winter wheat growing season of 2013-2014 at the Experimental Station of Jiangsu Academy of Agricultural Sciences, Nanjing (32°2ʹN, 118°52ʹ28ʺE), China. Two representative local wheat cultivars, Ningmai 13 and Yangmai 13, were grown in plastic pots with height at 20 cm and diameter at 25 cm. Each pot was filled with 12 kg of air-dried soil, and seven small holes (1 cm in diameter) were drilled to drain excess water. The soil contained 13.7 g/kg organic carbon, 54.95 mg/kg available nitrogen, 24.25 mg/kg Olsen-phosphorus and 105.03 mg/kg available potassium. Soils of each pot was pre-mixed with 0.7 g of N, 0.3 g of P 2 O 5 and 0.7 g of K 2 O, and another 0.4 g of N was applied at jointing stage to each pot. 12 seeds were sown to each pot on 5 November 2013, and then limited to four plants at the 3-leaf stage.

Four treatments were established: CK (control, non-stressed plants), WA (waterlogging alone), SA (shading alone) and WS (combined waterlogging and shading). The WA treatment was achieved by reserving a 1 cm water layer above soil surface (pots were placed in an artificial pool and the depth of water layer adjusted manually). For the SA treatment, a black polyethylene screen was fitted about 180 cm above the ground to block about 80% of the total radiation. The WS treatment was achieved by combining both waterlogging and shading treatments. All treatments started when jointing and grainfilling stage was reached (5 March and 18 April 2014, respectively), and there were three durations (5, 10 and 15 d respectively) for each treatment. At the end of each treatment, excess water was drained and the black polyethylene screen removed.

Two pots of each treatment and duration for both cultivars were retained until maturity (20 May 2014) for measurement of yield components. Generally, spike number per plant (SN), grain number per spike (GN), 1000-kernel weight (TKW) and grain yield per plant (GY) were recorded.

Statistics Analysis

Significant differences between the treatments was determined by one-way analysis of variance. P<0.05 was used as the standard for significance by least significant difference (LSD). Statistical analysis was performed using the SPSS 16.0.

Results

Effects of waterlogging and shading on wheat grain yield at jointing stage

All three treatments had a limited effect on SN at jointing stage. Significant difference was seldom shown between treatments, cultivars and treatment durations (Fig. 1). GN and TKW showed no significant differences for both cultivars under SA (Fig. 2, Fig. 3). There was a significant reduction of GN for both cultivars under WA (Fig. 2). Ningmai 13 showed good tolerance to WA and WS with no significant decreases of TKW. However, TKW of Yangmai 13 significantly decreased under both WA and WS (Fig. 3). With increased duration of WA treatment, GY of both cultivars decreased (Fig. 4). After 15 d of waterlogging, GY significantly decreased (14.25 to 9.57 g and 15.97 to 9.40 g for Ningmai 13 and Yangmai 13, respectively; P<0.05). The effects of SA and WS on GY depended on the cultivar. GY of Ningmai 13 under SA and WS decreased non-significantly compared with CK. However, there were greater decreases in GY for Yangmai 13 under SA and WS, and decreases were significant when duration reached 10 d. Under all stresses, GY was significantly reduced for Yangmai 13 and was lower than for Ningmai 13 (Fig. 4). Thus, Ningmai 13 had better tolerance to stresses than Yangmai 13 at jointing stage. Wheat yields under different treatments showed an order of CK > SA > WS > WA at jointing stage. Ningmai 13 had a significant yield reduction only under WA, mainly due to the reduction of GN. Yangmai 13 had significant yield reductions under all three treatments but for different reasons: yield reduction was caused by decrease of GN and TKW under WA; by decrease in GN under SA; and by decrease in TKW under WS.

Effects of waterlogging and shading on wheat grain yield at grain-filling stage

All three treatments at grain-filling stage showed non-significant effects on SN (Fig. 5), which is similar to that at jointing stage (Fig. 1). The reason is mainly due to that SN had been determined before jointing and grain-filling stages. Therefore, waterlogging and shading have a limited effect on SN. Waterlogging and shading showed a weak effect on GN at grain-filling stage. Significant but limited reductions of GN were observed only for Ningmai 13 under WA and SA (Fig. 6). All three treatments caused reduction on TKW compared with CK at grain-filling stage. And significant reduction was shown after 15 d of stresses (Fig. 7). Moreover, TKW under different treatments showed an order of SA> WA > WS. The results indicated that WA caused a more severe damage to grain-filling compared with SA, while shading caused an addictive damage when accompanied with waterlogging at grain-filling stage. GY of both cultivars showed a reduction when suffering waterlogging at grain-filling stage. After 15 d of WA, GY significantly decreased compared with CK (14.25 to 9.2 g and 15.97 to 11.48 g for Ningmai 13 and Yangmai 13, respectively; P<0.05). WS showed a similar reduction of GY compared with WA when the stress duration is shorter than 10 d, but a larger reduction when duration reached 15 d (Fig. 8). Wheat yields under different treatments showed an order of CK > SA > WA > WS at grain-filling stage, which was different from the order at jointing stage. Three treatments all caused significant yield reduction for both cultivars when durations were over 10 d. The yield reduction was mainly due to the reduction of TKW.

Discussion

Waterlogging caused severe yield reduction at both jointing and grain-filling stages and the reduction amplified with increased duration. After 15 d waterlogging treatment, yield of Ningmai 13 and Yangmai 13 was significantly reduced by 28-41% compared to CK at two growth stages (P<0.05), which is similar to former studies [START_REF] Hossain | Poor grain filling induced by waterlogging is similar to that in abnormal early ripening in wheat in Western Japan[END_REF] . However, the yield reduction was due to different reasons at both stages. Waterlogging caused yield reduction mainly results from the reduction of GN at jointing stage, indicating that waterlogging at jointing stage would influence the booting stage. At grain-filling stage, SN and GN have been determined. Thus, yield reduction was mainly results from the reduction of TKW. After 15 d WA treatment at grain-filling stage, TKW of Ningmai 13 and Yangmai 13 were 27.76 and 33.88 g respectively, which is lower than CK (42.51 and 46.89 g) and the same treatment at jointing stage (36.39 and 40.57 g). The result indicated that grain-filling process was severely impaired when suffering waterlogging stress at grain-filling stage.

Due to the increases in aerosols and air pollutants, the reduction rate of solar radiation is approximate 2.6% per 10 years [START_REF] Huan | Long-term Low Radiation Decreases Leaf Photosynthesis, Photochemical Efficiency and Grain Yield in Winter Wheat [J][END_REF] . Shading becomes a constraint of winter wheat production in the Yangtze River Basin [START_REF] Zhiqing | Characteristics of climate change during wheat growing season and the orientation to develop wheat in the lower valley of the Yangtze River [J][END_REF] . Results of the present study also showed that shading would cause yield reduction of winter wheat. Shading at grain-filling stage induced a greater reduction than at jointing stage. The difference is mainly due to the duration of recovery. After removal of stresses at jointing stage, there was a recovery for more than 60 d until harvest. But at grain-filling stage, the recovery duration was less than 30 d. The shorter recovery duration resulted in the significant reduction of TKW under SA treatment at grain-filling stage, thus the yield reduction was greater than that at jointing stage.

Compared with independent waterlogging and shading stresses, the combined stress (WS) is a more realistic situation in a continuous rain event. Results of combined stress treatment showed that WS at different growth stages induced different effects on wheat yield. WS had a less negative effect compared with WA at jointing stage, whereas a greater negative effect at grain-filling stage. The result indicated a compensative effect of shading on waterlogging at jointing stage, but an additive effect at grain-filling stage. The difference was mainly due to the process of recovery. Recent studies indicate that mechanisms of plant resistance to waterlogging and subsequent recovery are highly different [START_REF] Setter | Review of prospects for germplasm improvement for waterlogging tolerance in wheat, barley and oats[END_REF] . The compensative effect from shading might be for both morphological and biochemical reasons [START_REF] Huan | Long-term Low Radiation Decreases Leaf Photosynthesis, Photochemical Efficiency and Grain Yield in Winter Wheat [J][END_REF] . Therefore, shading at jointing stage showed a limited effect on wheat yield and had a compensative effect under WS. However, compensative effect of shading was impeded due to natural leaf senescence at grain-filling stage. Thus a different effect from shading was shown at different stages.

Conclusions

Independent and combined stresses of waterlogging and shading all caused the yield reduction of winter wheat, and stresses at different stages have different impacts. WS caused a less severe reduction than WA at jointing stage whereas a more severe reduction at grain-filling stage. The results indicated that shading had a compensative effect on waterlogging at jointing stage, but an addictive effect at grain-filling stage. The different effect is mainly due to recovery process of winter wheat after removal of stresses. However, most present wheat growth models are based on the experiment of waterlogging alone, without considering the effect of shading during continuous rain events. Thus, the present study advances the understanding on the physiology of winter wheat under combined stress of waterlogging and shading. Winter wheat growth models could be improved to avoid overestimating or underestimating production losses due to continuous rain.

Fig. 1 .

 1 Fig. 1. Effects of different treatments (CK, WA, SA and WS) on spike number per plant at jointing stage Different letters represent Significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 2 .

 2 Fig. 2. Effects of different treatments (CK, WA, SA and WS) on grain number per spike at jointing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 3 .

 3 Fig. 3. Effects of different treatments (CK, WA, SA and WS) on 1000-kernel weight at jointing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 4 .

 4 Fig. 4. Effects of different treatments (CK, WA, SA and WS) on grain yield per plant at jointing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 5 .

 5 Fig. 5. Effects of different treatments (CK, WA, SA and WS) on spike number per plant at grain-filling stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 6 .

 6 Fig. 6. Effects of different treatments (CK, WA, SA and WS) on grain number per spike at grain-filing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 7 .

 7 Fig. 7. Effects of different treatments (CK, WA, SA and WS) on 1000-kernel weight at grain-filing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Fig. 8 .

 8 Fig. 8. Effects of different treatments (CK, WA, SA and WS) on grain yield per plant at grain-filing stage Different letters represent significant differences at P<0.05. Error bars represent the standard deviation.

Acknowledgment

This study was funded by the Special Fund for Agro-scientific Research in the Public Interest (201203032), Jiangsu Province Science and Technology Support Program (BE2012391), and the Fund for Independent Innovation of Agricultural Sciences in Jiangsu Province (CX(12)3055).


Chaopeng Wang

Wenqian Huang
email: bhuangwq@nercita.org.cn

Baohua Zhang

Jingjing Yang
email: dyangjj@nercita.org.cn

Man Qian

Shuxiang Fan
email: ffanshuxiang1989@163.com

Liping Chen

Design and Implementation of an Automatic Grading System of Diced Potatoes Based on Machine Vision

Keywords: Computer vision, Diced potatoes grading, Image processing, Structured light, Three-dimensional measurement

Potato is one of the most important crops in the world. In recent years, potato and its processed products have gradually become important trade goods. As an important semi-manufactured product, diced potatoes need to be graded according to their three-dimensional (3D) size and shape before trading. 3D information inspection manually is a time-consuming and labor intensive work. A novel automatic grading system based on computer vision and near-infrared linear-array structured lighting was proposed in this paper. Two-dimensional size and shape information were extracted from RGB images, and height information was measured in NIR images combined with structured lighting. Then, a pair of pseudo-color and gray level height map images fusing with 3D size and shape information was constructed. Finally, diced potatoes were classified into either regular or irregular class according to their 3D information and criteria required by the industry. The grading system and proposed algorithm were testified by a total of 400 diced potatoes with different size and shapes. The test results showed that the detection error was in the range of about 1 mm, and the classification accuracy was 98%. The results indicated that the system and algorithm was efficient and suitable for the 3D characteristic inspection of diced potatoes.

Introduction

Potato is the World's and China's fourth largest staple crop after rice, wheat and maize, it is also one of the most promising high-yield crops in China [START_REF] Qu | Development of potato industry and food security in China[J][END_REF]. With the rapid development of global agriculture, potato and its processed products have great market potential and become a primary part of the global agricultural trade as well.

Consumers care more and more information about the products they bought, and wellinformed high-quality products are easier to arouse consumers' interest and stimulate their purchasing desire [START_REF] Zhang | Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3Dreconstruction[END_REF]. For fruits and vegetables, appearance is one of the most important sensory quality attributes, it would not only influences the packaging and retail price, but also affects consumers' preferences and choice. Products with perfect appearance are always receive more favor of consumers favor and would have a better sales appeal [START_REF] Elmasry | In-line sorting of irregular potatoes by using automated computer-based machine vision system[END_REF][START_REF] Zhang | Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier[END_REF]. Diced potato as a kind of semi-manufacture in trade has strict requirements in size and shape. The irregular shape of diced potato not only deteriorates its appearance, but also has effects on the market value. Therefore, it is necessary to make classification for diced potatoes according to the 3D information before packaging or trading. In practice, diced potatoes are classified by human visual inspection, and 3D information inspection manually is a labor intensive work, it was also time-consuming. Manual processing pose added problems of maintaining the consistency and uniformity in grading [START_REF] Ohali | Computer vision based date fruit grading system: Design and implementation[END_REF][START_REF] Blasco | Machine vision system for automatic quality grading of fruit[END_REF].

Currently, with the improvement of image processing, quality control with computer vision has been an important technology [START_REF] Razmjooy | A real-time mathematical computer method for potato inspection using machine vision[END_REF][START_REF] Kong | Potato grading method of mass and shapes based on machine vision[END_REF]. For the requirement of speedy and real time, computer vision system has being developed as a significant part in quality detection and evaluation [START_REF] Gunasekaran | Computer vision technology for food quality assurance[END_REF][START_REF] Zhang | Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review[END_REF]. Over the past several years, the on-line inspection system based on computer vision are widely used in realization of automatic detection of many different agricultural products, including the external and internal quality detection, but automatic classification for diced potato according to their 3D information is still not available. There is an increasing demand for on-line detection equipment based on computer vision that can mimic the human grading and realize accurate classification of diced potato with the unified standard to address the above issues with human visual inspection for food manufacturers.

With photo-electronics, image processing and computer technique rapid development, structured light vision technology has been widely used in computer vision system for 3D reconstructionandautomatic measurement successfully [START_REF]Complete calibration of a structured light stripe vision sensor through planar target of unknown orientations[END_REF]. There have been several techniques proposed with quite different characteristics for accurate measurement. In order to get the height of seedling, Feng et al.designeda automatic inspection system based on structured light vision, and result shows the height measurement error was less than 5 mm for the normally straight seedling [START_REF] Feng | Development and experiment on system for trayseedling on-line measurement based on line structured-light vision[END_REF]. However, the detection precision could not satisfy the requirement of diced potato inspection. Binocular visual was also extensive used for 3D size measurement. A binocular structure-light scanner was constructed to acquire the surface detail information of the work-pieces by Liu et al., and it was suitable for precision and efficiency demands of the large work-piece measurement [START_REF] Liu | Industrial vision measuring system for large dimension work-pieces[END_REF]. However, 3D measurement based on binocular stereovision systems is not suitable used for onlinedetection of diced potato just because it is time consume and the complexity of stereo matching. Coded structured light systems were commonly used for real-time acquisition of 3D surface data and it was considered as an important and widely used active shape acquisition technique [START_REF] Chen | Survey on Structured Light Pattern Codification Methods[END_REF][START_REF] Salvi | A state of the art in structured light patterns for surface profilometry[END_REF]. The encoded pattern palys a dominate role in the system, it could affect all the measurement performances, including the time consumptionandaccuracy. Xu et al. proposed one-shot pattern method for 3D shape measurement,result shows that the accuracy can achieve 0.18 mm.With the system,moving object could be inspected, and it can be implemented for automotive production lines [START_REF] Xu | Real-time 3D shape inspection system of automotive parts based on structured light pattern[END_REF]. A principle of uniquely color-encoded pattern projection was proposed by Chen et al. [START_REF] Chen | Vision processing for realtime 3-D data acquisition based on coded structured light[END_REF] to design a color matrix for improving the reconstruction efficiency based on coded structured light system. By using such a light pattern, it could realize the 3D vision reconstruction from a single image and accomplish the reliable and accurate measurement for scene objects. It could also be used in dynamic environment for real-time application. However, encoded structured light system is complex and the light pattern has important effect on measurement accuracy.

To realize automatic on-line classification of diced potatoes, a grading system based on computer vision with high accuracy and reliability performance is needed. For this purpose, intensive research works are being conducted to design and build a flexible, reliable and effective computer vision system by using a monocular stationary camera anda near-infrared linear-array structured lighting.

2Objectives

In order to develop a real time grading system for diced potato classification by using computer vision and near-infrared linear-array structured lighting. To actualize this objective, several steps have to be completed: (1) RGB and NIR images synchronous acquisition through the same optical path of the camera for 3D information inspection. (2) 2D size measurement in RGB images for width and length inspection. (3) 2D shape feature (rectangle degree) extraction from the RGB images for contour shape inspection. (4) Height map (pseudo-color image and gray level images) construction according to the height information extracted from NIR images by using near-infrared light. (5) Evenness evaluation by using Gaussian distribution in height maps (gray level images). ( 6) Developing an efficient image processing algorithm based on computer vision system and near-infrared structured light to classify the diced potato into either regular or irregular class.

3Materials and methods

Diced potato samples

Fresh potatoes from a market in Beijing, were selected and processed into diced potatoes for study. The shape of some diced potatoes were influenced by the surface of potato, as a result, the contour shape of these diced potatoes would be triangle or other irregular shape, and the surface would be slant or rugged. Diced potatoes were classified into either regular or irregular class according to their 3D information and criteria required by the packinghouse. Namely, regular diced potatoes, which were traded at a higher price, can meet all the requirements of the geometric parameters, and irregular that doesn't satisfy at least one of the criteria. In our study, 17 potatoes were processed into 400 diced potatoes (length, width and height were about 15 mm for regulars, the geometries were uncertain for irregulars), including 270 regular and 130 irregular samples.

3.2Computer vision system

All the samples were inspected and classified by using the vision system as shown in Fig. 1(a). Thevision system used in our study issameas the system described in [START_REF] Zhang | Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3Dreconstruction[END_REF]. It mainly consists of a 2CCD camera (JAI AD-080GE), a near-infrared linear-array structured lighting (800nm, 200mw), a lighting system (LED light), a computer, a conveyor belt driven by a stepper motor. The multi-spectral camera installed right above the conveyor belt was connected to network ports of computer with two RJ45 twisted-pairs as show in Fig. 1 (b). It can acquire both NIR (800nm) andRGB imagesthrough the same optical path simultaneously. The structured lighting was mounted on the upper left of conveyor belt and in the same horizontal plane with camera. The pair of two visible LED light source were distributed symmetrically at the both upper sides of conveyor for light supplement. The whole system was placed in a black box to prevent the interference from outside. In the process of 3D information detection, the image acquisition, image processing, final diced potatoes classification and conveyor belt control panel proposed in this paper was developedinMFCcombined withOpenCV.

The conveyor belt consists of inferior smooth material belt and stepper motor was used to transmit diced potatoes for the on-line detection. The specular reflection of projection light strip would be significantly reduced due to the inferior smooth surface of belt. And this would make it easy to extract the light strip and reduce the inspection error. The stepper motor was controlled by the driver and controller which were connected to computer through RS232 serial port to control the speed and other motion parameters of conveyor belt.

3.32D shape inspection methods

Contour shape detection plays a domination role in 2D shape extraction. The majority of diced potatoes are regular, and the contour shape of qualified diced potato is regular rectangle. For others, they are influenced by the surface of potato, and the contour shape would be irregular. If the contour shape was not rectangle, the diced potato under detection would be judged as irregular directly and other shape information was not needed. Rectangle degree is a significant index to measure the contour shape of rectangular objects. Rectangle degree reflects the filling degree of an object to its external rectangle and rectangle factor can be used to distinguish whether the aim region is rectangle [START_REF] Rosin | Measuring shape: ellipticity, rectangularity, and triangularity[END_REF]. Therefore rectangle degree was used for the contour shape judgment with minimum circumscribed rectangle method in this paper. 𝑅 = 𝑆 0 𝑆 𝑀𝐸𝑅 ⁄ (1) Where, R represents the rectangle factor, S 0 represents the area of diced potato's surface imaged by camera, S MER represents the area of minimum circumscribed rectangle. For rectangular objects, the rectangle factor is infinite closed to 1, but for other objects, the rectangle factors vary from 0 to 1. The inspection principle of height information based on triangulation theory is shown in Fig. 2. The conveyor belt is regarded as the reference plane,and it is configured to be parallel with the baseline of the camera and laser projector. The theory of height measurement can be explained by similar triangles △ABP and △CDP. According to the triangle similarity, height of objectcan be calculated by:

3.43D size inspection methods

ℎ 𝐿 -ℎ = 𝑑 𝑠 (2)
Where, h represents the distance from point Q to C, and it also represents the height of object; d represents the distance between points Cand D which can be extracted by image processing; L represents the vertical distance from the baseline of camera and laser projector to the conveyor belt; s represents the baseline distance from the laser projector to the CCD camera; L and s can be measured directly. Equation ( 2) can be equally transformed into the format as:

ℎ = 𝐿 -ℎ 𝑠 𝑑 (3)
Generally, the height of diced potato is lower than 20 mm, but the distance between baseline of camera and laser projector to the conveyor belt has exceeded 500 mm, large difference existed between them in magnitude. So equation (3) can be simplified as:

ℎ = 𝐿 𝑠 𝑑(4)
For height measurement, the distortion distance d'caused by height of diced potato should be extracted for offset distance d detection. And it can be calculated by the distancebetween the projecting light stripes in inspected and reference images. The xcoordinate of point Q(x, y) is same of point C(x, y) due to the light strip is parallel to the y-axis of camera image plane, and it can be obtained before inspection. For points B, P, and D is collinear, and it is the same pixel for bothreference plane point D and inspected part point P in the camera image plane, point P can be easily detected by image processing. A reference image with straight light strip should be acquired before inspection to obtain the reference coordinates Q(x, y), for which the coordinate position is fixed. In height measurement process, when diced potatoes were transmitted through the view of camera, NIR images with the distortional light strip should be acquired to get the information of detection coordinates P(x, y). The difference between x-coordinate of points P and Q could represent the distortion distance d' due to points P and Q were collinear, they had the same y-coordinate.

Both RGB and NIR images were used for 3D size detection. RGB images were processed for the 2D size measurement. With minimum circumscribed rectangle method, width and length of diced potato could be simply and conveniently detected in a small error range. NIR images were processed for height measurement. Fig. 3 shows NIR images acquired by the monocular camera. When nothing is under detection, the light strip would be continuous without any distortion for the nearinfrared structured light is just throwing lighting onto reference plane as shown in Fig. 3(a). But when diced potato is transmitted and through the vision system, parts of the light strip would project on the surface of it, and the light strip would be disconnected as shown in Fig. 3(b). The offset distance of light strip would change with the height of diced potato, so it is important to get the relationship between diced potato's actual height and the offset distance of light strip for height measurement. Before the height measurement, threshold segmentation was used to carry on binary processing for NIR image. And then, light strip in binary images should be refined for pixel coordinate extraction. An applicable image thinning algorithm not only need to provide high inspection accuracy but also should has high efficient that could complete the image processing in the time interval between two images was acquired (the maximum frame rate of AD-080GE is 30 fps). In our research, the centerline extraction method was used for the image thinning. The center pointcoordinatesO(x 0, y 0 )of light strip in each line of binary image can be calculated using equations [START_REF] Ohali | Computer vision based date fruit grading system: Design and implementation[END_REF]:

𝑥 0 = 1 𝐴 ∑ 𝑥 (𝑥,𝑦)∈𝑅 𝑦 0 = 1 𝐴 ∑ 𝑦 (𝑥,𝑦)∈𝑅 (5)
Where,R represents the pixels set of light strip in each line of binary image; A represents the size of pixels set;xand y represents the x-coordinate and y-coordinate of each pixel in light strip.

NIR images were detected line-by-line to get pixel coordinates of the centerline which could represent the position of light strip. After thinning of the binary images, the centerline coordinates could be extracted, and distortion distance d' could be calculated by: 𝑑 ′ = 𝑑 0 -𝑑 1 (6) Where, d 0 represents the centerline coordinates of reference image, d 1 represents the centerline coordinates of inspected image.

In our research, model materials (50 mm long, 30 mm wide and 2 mm high) were used layer by layerand formed different heights for the data sampling.Cubic spline interpolation was used to establish the relationship between offset distance d and distortion distance d' by using Matlab 2010. When d' were inspected by visual inspection system, the offset distance of light strip would be got by the fitting function, which could represents the relationship. Finally, the real height of diced potato h would be calculated with triangulation theory proposed above.

3.5Surface evenness detection

For further research, height map, including pseudo-color and gray level images were constructed according to the distortion distance d' in the process of height measurement. The pseudo-color images could make it easier for human eyes to evaluate the performance of the height image, and the gray level images were used for the diced potato's surface evenness detection. The surface evenness was assessed based on the grey scale distribution of height map. The average and mean square error of d' could be calculated by the grey scale distribution, which could be extracted by the image processing of gray level image. Gaussian model was used for surface evenness detection, and the evenness was determined by the ucl (upper control limit) and lcl (lower control limit) of d' using equations ( 7 Where, i represents the gray scale of gray level image from 1 to 255, gray scale 0 is preclusive due to it is defined as the background (black) of the height image; p (i) represents the appearing probability of each gray scale appeared in height map image.

As show in Fig. 4, the shadow represents the gray scales in the scope of lcl and ucl. If the total probabilities of gray scales in the shadow area is higher than a specified threshold, the surface evenness would be judged as eligible.

3.6Whole classification algorithm for 3D size and shape inspection

In this paper, 3D geometry characteristics of diced potatoes were extracted by using a combination of structured lighting method, triangulation theory and minimum circumscribed rectangle method. The image processing and shape classification algorithm mainly include the followingsteps: (1) Calibration: NIR image in which the light strip was uninterrupted was acquired before shape detection to get reference pixel coordinates; (2) 2D size measurement: RGB image was processed to get the length and width in pixels with minimum circumscribed rectangle method. Then the length and width of diced potatoes would be figured out with the parameters of camera and other parameters. (3) 2D shape feature extraction: RGB images were processed to get the rectangle degree of diced potatoes, according which to judge if the contour shape of diced potato was regular rectangle. (4) Height map construction: Pseudo-color images and gray level images which could represent the height information of diced potato were constructed simultaneous depend on the distortion distance d' between the projection light stripes in inspected andreference images. ( 5) Surface evenness evaluation: The grey-histogram of height map (gray level image) was created to get average of d' and mean square error of d', and then judge if the surface of diced potato was even with Gaussian model. ( 6) Grade judgment: All the shape parameters inspected through above steps were compared with the parameters of 3D characteristic information set according to the factory requirements for classification. If the geometrical information measured by computer vision system satisfied requirements, Flag would be defined as TRUE, and diced potato under detection would be regarded as regular. Otherwise, the Flag would be defined as FLASE and the diced potato would be regarded as irregular. Finally, diced potatoes were classified into either regular or irregular class. The flowchart of the whole classification algorithm is shown in Fig. 5.

RGB+NIR image acquisition

4Results and discussion

The automatic control software based on computer vision is shown in Fig. 6.The online inspection of diced potato would be conducted according to the classification algorithm mentioned above. Inspection results, including 3D size and classification information were shown in real time. Meanwhile, the height map images could be constructed with the moving of diced potatoes, and the pixels would be assigned to different color according to their height information and color bar. is the relevant images of an irregular diced potato.As shown in Fig. 8(f) and 8(g),the height mapwere distorted due to the slant or rugged surface, and the surface area of irregular diced potato was larger than the surface area ofregular diced potato.Compared Fig. 8(d) with 8(h), it is obvious that,if diced potato under detection was regular, the grey scale distribution would be relatively concentrated and its' distribution trends is similar to Gaussian distribution. Otherwise, the grey scale distribution would be dispersive for irregular diced potato with the uneven surface. Fig. 9 shows the detection results of geometric parameters. Fig. 9(a), 9(b) and 9(c) shows the relationship of real values and measured values of length, width and height, respectively. The corresponding R 2 (coefficient of determination) is 0.9072, 0.8939 and 0.8968, this could meets requirements for the classification of diced potatoes. The rectangle degree and Gaussian probability were the other two important parameters to judge if diced potato was regular or not. For most irregular diced potatoes, these two parameters were influenced by the surface of potato. There has a relationship between the contour shape and surface evenness, it can be represented by the correlation between rectangle degree and Gaussian probability. As shown in Fig. 9(d), it is obvious that the rectangle degree of regular diced potato is higher than 0.9. By contrast, the rectangle degree of irregular diced potato is disperse because of the various contour shape, and for majority of them, the rectangle degree is lower than 0.9. The Gaussian probability of regular diced potato is higher than 0.68, and it is lower than 0.68 for major irregular diced potatoes due to their uneven surface. Therefore, 0.68 can be used as the decision condition for the surface evenness judgment. For minority irregular diced potatoes, the Gaussian probability is higher than 0.68, but 3D size and rectangle degree could not meet the requirements, it is greatly reduced the inspection error probability. In Fig. 9 (d), there have many regular diced potatoes with the same rectangle and Gaussian probability, therefore some overlap was also observed. The irregular diced potatoes mainly include 3 types: (1) Geometrical feature is regular, but 3D size would not satisfy the requirements as show in sample 3. Though the contour shape is regular rectangle and the surface is evenness, the height could not meet requirements. This is obvious when compared the height map of sample 3 with sample 1 or 2, especially in pseudo-color image. (2) Both of the contour shape and surface evenness could not satisfy the requirements as shown in sample 4, this would be classified as irregular directly with rectangle degree detection. (3) The contour shape of diced potato is regular rectangle, but the surface is slant or rugged, as shown in sample 5 or 6. It is obvious to observe the trend of the surface height change with the height map. Real-time, on-line, low-cost computer vision system is urgent needed to replace the manual inspection for the classification of diced potato. An automatic grading system based on computer vision and near-infrared linear-array structured lighting was proposed in this paper. In order to reduce the interference of external conditions and make the inspection results more precise and stable, visible and near-infrared lineararray structured lighting were used respectively for contrast tests to select the projector with higher detection speed and accuracy. Meanwhile, convey belt with inferior smooth material was used for diced potatoes transmission. The matt surface would reduce the specular reflection of projection light strip significantly and this would make it easy to extract the light strip. In this research, RGB and NIR images acquired simultaneously by a monocular camera were processed for 3D geometrical characteristics extraction, including physical dimension, contour shape and surface evenness, which were used as the main decision conditions for the classification. Minimum circumscribed rectangle method and structured light measurement method were used for the 3D size inspection, meanwhile, height map include pseudo-color image and gray level image were constructed according to the height of diced potato for the surface evenness judgment with Gaussian module. The results demonstrate the detection error of the grading system was in range of about 1 mm, and the classification accuracy has reached 98%. This preliminary research verified the possibility and feasibility of using computer vision combined with structure light measurement method for the classification of diced potato.

Fig. 1 .

 1 Fig.1.Schematic illustration of computer vision system (a)Diagram of the computer vision system used in this research (b) Connection diagram of monocular camera and computer

Fig. 2 .

 2 Fig. 2.The theory of the height measurementbased on triangulation principle

Fig. 3 .

 3 Fig. 3.NIR images acquired by AD080-GE (a) Reference image (b) Detection image

  ) & (8): 𝑢𝑐𝑙 = 𝜇 + 𝜎(7) 𝑙𝑐𝑙 = 𝜇 -𝜎[START_REF] Kong | Potato grading method of mass and shapes based on machine vision[END_REF] Where, μ represents the average of d', σ represents the mean square error of d'. Due to different distortion distance d' corresponds to different gray scale in gray level image, μ and σ can be determined using equations (9) & (10):

Fig. 4 .

 4 Fig. 4.Control limits for surface evenness detection

CalibrationFig. 5 .

 5 Fig. 5.Flowchart of the classification algorithm for diced potato

Fig. 6 .

 6 Fig. 6.The inspection system developed in VC++ 2010 for diced potato grading

Fig. 7 .

 7 Fig. 7.The result of light strip thinning and data smoothing (a)Detection image (b) Result of image thinning (c) Original data of distortion distance d' (d) Results data after median filtering

Fig. 8 .

 8 Fig. 8.Detection results of Regular and irregular diced potato detection (a) RGB image of regular diced potato; (b) Pseudo-color image of regular diced potato; (c) Gray level image of regular diced potato; (d) Gray histogram of regular diced potato; (e) RGB image of irregular diced; (f) Pseudo-color image of irregular diced potato; (g) Gray level image of irregular diced potato; (h) Gray histogram of irregular diced potato In order to observe the classification effect, and testify detection algorithm proposed in this paper, the inspection results of different classes of diced potatoes were randomly selected. Measuring error was extracted by comparing the 3D size detected by the grading system with the real value measured by digital caliper. The measurement results, including length, width and height shows minor difference (in range of 1mm) contrasted with the real value.Fig.9shows the detection results of geometric parameters. Fig.9(a), 9(b) and 9(c) shows the relationship of real values and measured values of length, width and height, respectively. The corresponding R 2 (coefficient of determination) is 0.9072, 0.8939 and 0.8968, this could meets requirements for the classification of diced potatoes. The rectangle degree and Gaussian probability were the other two important parameters to judge if diced potato was regular or not. For most irregular diced potatoes, these two parameters were influenced by the surface of potato. There has a relationship between the contour shape and surface evenness, it can be represented by the correlation between rectangle degree and Gaussian probability. As shown in Fig.9(d), it is obvious that the rectangle degree of regular diced potato is higher than 0.9. By contrast, the rectangle degree of irregular diced potato is disperse because of the various contour shape, and for majority of them, the rectangle degree is lower than 0.9. The Gaussian probability of regular diced potato is higher than 0.68, and it is lower than 0.68 for major irregular diced potatoes due to their uneven surface. Therefore, 0.68 can be used as the decision condition for the surface evenness judgment. For minority irregular diced potatoes, the Gaussian probability is higher than 0.68, but 3D

Fig. 9 .

 9 Fig. 9.Detection results of geometrical characteristics (a) Comparison of measured length and real length; (b) Comparison of measured width and real width; (c) Comparison of measured height and real height; (d) Distribution of Gaussian probability and rectangular ratio for regular and irregular diced potato The image processing mainly includes minimum circumscribed rectangle detection and height map construction aim at getting the 3D geometrical characteristics of diced potato.The intermediate process result images are shown in Fig. 10. Diced potatoes with different shape as shown in Row(a), all the geometrical information of sample 1 and 2 are regular and could satisfy the criteria, and other samples are irregular. Images in Row (b) are the detection results with minimum circumscribed rectangle method. Height map including pseudo-color and gray level image constructed in the process of height measurement are shown in Row (c) and Row (d), different height is denoted as different pixel values.The irregular diced potatoes mainly include 3 types: (1) Geometrical feature is regular, but 3D size would not satisfy the requirements as show in sample 3. Though the contour shape is regular rectangle and the surface is evenness, the height could not meet requirements. This is obvious when compared the height map of sample 3 with sample 1 or 2, especially in pseudo-color image. (2) Both of the contour shape and surface evenness could not satisfy the requirements as shown in sample 4, this would be classified as irregular directly with rectangle degree detection. (3) The contour shape of diced potato is regular rectangle, but the surface is slant or rugged, as shown

Fig. 10 .

 10 Fig. 10.Results of diced potatoes with deferent shape (a) RGB images; (b) Results of minimum circumscribed rectangle method; (c) Pseudo-color images; (d) Gray level image

Acknowledgment

The authors gratefully acknowledge the financial support provided by National Key Technology R&D Program (No. 2014BAD21B01).