Juan Yang
email: yangjuan@saas.sh.cn

Jingyin Zhao

Qian Guo

Yunsheng Wang

Ruijuan Wang

Data acquisition method for measuring mycelium growth of microorganism with GIS *

Keywords: mycelium, data acquisition, microorganism, Pleurotus eryngii

Mycelium is the vegetative part of a fungus or most microorganisms, consisting of a mass of branching, thread-like hyphae. It is through the hyphae that a fungus absorbs nutrients from its environment. For most fungi, the ability of nutrition translation from mycelium to fruit body is determined by growth status of hyphae. It is very necessary to study the effect of environmental factors on mycelium growth, and know the befitting environment condition. However, finding a good data acquisition method for measuring the mycelium is the key point.

A new method was introduced in the paper. The method is using image identification and space data analysis function of the GIS to acquire development rate of mycelium i.e. hyphae. Pleurotus eryngii under commercial production is taken as example. The effect of different temperature and humidity on mycelium growth was analyzed. It is hoped to explore a new method for scientific and precise measurement the growth status and development rate of mycelium.

Introduction

Mycelium is the vegetative part of a fungus or most microorganisms, consisting of a mass of branching, thread-like hyphae. It is through the hyphae that a fungus absorbs nutrients from its environment. Hyphae are very wispy and only several microns long. The structure of hyphae only can be observed by microscope, so the growth of hyphae is usually expressed by morphologic change of mycelium. There are two methods to measure mycelium growth at present. The first method is physical method. By checking the space change of the mark on the forepart of a mycelium or the diameter change of a mycelium in certain period of time, the growth of the mycelia can be observed. However, this method has a big error, moreover, it only adapts to the smooth agar substrate in laboratory [START_REF] Bending | The structure and function of the vegetative mycelium of ectomycorrhizal plants .Activities of nutrient mobilizing enzymes in birch litter Ⅵ colonized by Paxillus involutus (Fr.)[END_REF] and can not be used in nature or in production of edible fungi. For the circumstances outside the laboratory, the substrate is often rough surfaced soil or the admixture of wood chip, corncob chip and so on. Another approach is to measure the fungal-specific biochemical markers [START_REF] Olsson | Signature fatty acids provide tools for determining of the distribution and interactions of mycorrhizal fungi in soil[END_REF], which is classified as chemical method. The signature fatty acid 18:2ω6,9 [START_REF] Olsson | Lipid and fatty acid composition of hyphae and spores of arbuscular mycorrhizal fungi at different growth stages[END_REF], ergostrrolp [START_REF] Nylund | Ergosterol analysis as a means of quantifying mycorrhizal biomass[END_REF] and chitin [START_REF] Ekblad | Determination of chitin in fungi and mycorrhizal roots by an improved HPLC analysis of glucosamine[END_REF] have been used as a marker for ectomycorrhizal(EM) fungi [START_REF] Wallander | Estimation of the biomass and seasonal growth of external mycelium of ectomycorrhizal fungi in the field[END_REF][START_REF] Pål | Lipid and fatty acid composition of hyphae and spores of arbuscular mycorrhizal fungi at different growth stages[END_REF], and the neutral lipid fatty acid 16:1ω5 has been used as a marker for arbuscular mycorrhizal(AM) fungi [START_REF] Pål | Lipid and fatty acid composition of hyphae and spores of arbuscular mycorrhizal fungi at different growth stages[END_REF]. For example, ergosterol is a fungus specific lipid used as a marker for living fungal biomass [START_REF] Nylund | Ergosterol analysis as a means of quantifying mycorrhizal biomass[END_REF][START_REF] Ekblad | Chitin and ergosterol combined to measure total and living biomass in ectomycorrhizae[END_REF], by quantifying its ergosterol content where the activity of mycelium was determined [START_REF] Carrillo | Improving the Production of Ectomycorrhizal Fungus Mycelium in a Bioreactor by Measuring the Ergosterol Content[END_REF]. There is another situation for some fungi that the target production is the antiviral, antibacterial or antifungal substances from its secondary metabolites, such as Pycnoporus sanguineus, which produces an important secondary metabolite, cinnabarin. The growth of the fungus was represented by the cinnabarin production [START_REF] Baumer | Comparative study of mycelial growth and production of cinnabarin by different strains of Pycnoporus sanguineu[END_REF].

However, most fungi or microorganisms do not have the specific biochemical matters in them. How to quantitatively express the growth of mycelium i.e. hyphae? A new method named photogrammetry was introduced in the paper for measuring the growth of mycelium. The approach was also applied in the GIS data acquisition [START_REF] Heipke | Image Analysis for GIS Data Acquisition[END_REF]. The key of the approach is using image identification and space data analysis function of the GIS software. Then the mycelium of Pleurotus eryngii under commercial production was taken as example. The effect of different temperature and humidity on mycelium growth was analyzed. The aim of this paper is to explore a new method for scientific and precise measurement of growth status and development rate of mycelium.

Materials and Methods

Mycelium living in all kinds of substrate

The method is not only appropriate for measuring the growth of mycelium living in the smooth agar substrate in laboratory experiments, but also for the mycelium living in almost all kinds of substrate, for example, the mycelium of Agaricus bisporus(Lange) Sing. living in the soil, the mycelium of Pleurotus eryngii living in the admixture of wood chip, corncob chip and so on.

It needs to mention that the method is not applicable for EM fungi or AM fungi, in which the mycelium accretes with plant roots and forms a symbiont. There are no methods having been available to distinguish mycelia from EM fungi or AM fungi from saprotrophic fungal mycelia in soil [START_REF] Wallander | Estimation of the biomass and seasonal growth of external mycelium of ectomycorrhizal fungi in the field[END_REF]. Therefore, the amount of EM fungi or AM fungi is usually calculated by the chemical method, which is reflected by their specific compounds.

Image Acquisition

The strongpoint of this method is that it is no need to destroy the growth of mycelium or touch the mycelium in data acquisiton, so the process and trends of growth of hyphae can be monitored.

The image of mycelium can be obtained by digital cameras with at least 1024 × 768 pixels or 300 resolutions.

Image Processing

The obtained photo is usually a color image, which has three bands, respectively, red (band_1), green (band_2) and blue (band_3). Through opening the band image of the color image in the ArcMap software, the monochrome image of the color image can be obtained. In the monochrome image, each pixel has a gray value (usually between 0 and 255) that specifies a particular shade of gray. Black is 0 and white is 255.

Taking the mycelium of Pleurotus eryngii as example, the hyphae live in the substrate loaded in the bottle, and the mycelium revealed on the bottle mouth is a window that reflects the growth of the hyphae. The picture of the mycelium on the bottle mouth was taken and opened in the ArcMap software. In the band_3 (blue) image of the three band image, the contrast between mycelium and substrate is the biggest one(see Fig. 1). The second step is extracting the region to be analyzed on the photo by the Spatial Analyst Tools--Extraction of the ArcGIS. That is, the useless region is removed (see Fig. 2B) and the Object Image Layer is obtainded.

Data Acquisition

With the identification tool in the ArcGIS, the gray value of each pixel can be identified. Because the color of Pleurotus eryngii mycelium is white and the color of substrate is much darker, the gray value tends to 0 for the pixel of Pleurotus eryngii mycelium and to 255 for the pixel of the substrate. It is important to confirm the critical gray value between the mycelium pixel and the substrate pixel. With the critical gray value, a new raster layer can be got by using the Raster Calculator tool of the Spatial Analyst Tools in the ArcGIS (see Fig. 2C).

In the new raster layer, the pixel of mycelium is valued 1 and the pixel of substrate is valued 0 if input formula in the Raster Calculator tool shows " 'the Object Image Layer' > 'the critical gray value' ", or the pixel of mycelium is valued 0 and the pixel of substrate is valued 1 if input formula in the Raster Calculator tool shows " 'the Object Image Layer' < 'the critical gray value' ". Opening the Attribute Table of the raster layer, the raster count of two pixel values is displaying (see Fig. 3). The proportion of mycelium can be calculated through the raster count of mycelium divided by the sum of the raster count of mycelium and substrate. By monitoring the development of the proportion of mycelium in unit times, the development rate of hyphae can be expressed.

Application and Discussion

Background

An example about the effect analysis of temperature and humidity for mycelium growth is given to illustrate the application of the method.

There are two phases for the hyphae of Pleurotus eryngii under commercial production. In the first phase, the strains of Pleurotus eryngii are inoculated into the culture medium loaded in plastic bottle. Then the bottle with the strains is incubated under conditions at 25 , ℃ 70-75% RH about 25 days, and it is still kept in this situation about 10 days for afterripening after the hyphae spreading into the entire bottle. On top of the bottle, lid is removed and the surface of the culture medium is mechanically scratched to remove the exterior aerial mycelium and a 15mm layer of substrate. This process is used to induce uniform formation of primordia with synchronous mushroom production [START_REF] Estrada | Yield, size and bacterial blotch resistance of Pleurotus eryngii grown on cottonseed hulls/oak sawdust supplemented with manganese, copper and whole ground soybean[END_REF]. The opened bottles are placed in a production room controlled at temperature about 18 , 85 ℃ -95% RH. The hyphae are entering into the second development phase.

The second phase is very important, especially the hyphae in the surface of the culture medium are important because they will kink into the bud of mushroom. The air climate control systems for the growing rooms are designed and manufactured by Patron AEM in Netherlands in this study. These unique systems are able to control temperature, humidity and CO 2 concentration in growing rooms very precisely and efficiently.

In the experiment of the example in this study, treatments included temperature at 14 , 15 , 16 , ℃ ℃ ℃ 17 and 18 with 97% RH, and relative humidity at ℃ ℃ 89%, 91%, 93%, 95% and 97% at 16 . Three ℃ replicates for each treatment were set. The experiment was a 2 (supplement) × 5 (treatment) design with 3 replicates per treatment. The mycelium growth was measured daily (every 24 hours) at 8 bottles for each experiment. Each value is the mean of 24 measured results (3 replicates×8 bottles).

Results and Analysis

In the mycelium growth stage, the mycelium proportion showed remarkable change in different days (Tab.1). The mycelium growth followed the theoretical logistic growth curve exactly (Fig. 4). The effects of temperature on mycelium of Pleurotus eryngii over a range of temperatures from 14 ℃ to 18℃ are shown in Fig. 4A and the effects of humidity over a range of relative humidity from 89% to 97% are shown in Fig. 4B. The mycelium proportion showed a biggish difference at temperature from 14℃ to 18℃. As the temperature increases, the mycelium development rate grows faster. It is indicated that the temperature of 17℃ and 18℃for mycelium growth is much better than other temperatures. The mycelium development rate also grows faster as the RH increases. However, there is no difference in the mycelium proportion under RH from 89% to 97%.

The above results can also be shown from the statistically multiple comparisons (Tab. 2). At different temperature, the mycelium developed the fastest at 17 , followed by 18 , 16 , 15 and 14 . And ℃ ℃ ℃ ℃ ℃ there is remarkable difference for the mycelium proportion among 17℃ and 18℃, 16℃, as well as among 18℃, 16℃ and 15℃, 14℃. Under different humidity, the mycelium developed the fastest under 95% and 93% RH, followed by 97%, 89% and 91% RH. However, in the experience, it was found that the surface of the substrate was very dry under 93% RH during the late stage of mycelium growth, and the kinked buds of mushroom were less. Through comprehensive analysis of the data, it is believed that the optimal temperature and humidity conditions for mycelium growth in the growing room is 16℃ and above, and 95% RH and above.

Using this method, the growth of mycelium can be dynamically monitored with no mycelium being destroyed. In addition to this, with the same principle of the method in this paper, in combination with computer image analysis, microscopy can be used in the study of microorganism structure. And the latter method has been used to quantify the growth, number and shape of cells in the different tissues of ageing mushrooms [START_REF] Braaksma | Morphometric analysis of ageing mushrooms (Agaricus bisporus) during postharvest development[END_REF].

Conclusion

GIS can be used for a wide range of applications such as urban and regional planning, agriculture, and wildlife and natural resource management. GIS is capable of capturing, storing, manipulating, and displaying spatial reference information to allow for efficient data organization and access. The method introduced in the paper can be named photogrammetry. It mainly uses image identification and space data analysis function of the GIS.

Three steps are included in the method, that is, image acquisition, image processing and data acquisition. Thereinto, the last two steps are the key. Image analysis is also one of the major research tasks in photogrammetry currently [START_REF] Heipke | Image Analysis for GIS Data Acquisition[END_REF].

The method is not only appropriate for measuring the growth of mycelium living in the smooth agar substrate in laboratory experiments, but also for the mycelium living in almost all kinds of substrate. From the examples taken in the paper, it can be seen that the growth of mycelium can be dynamically and quantatively monitored with no mycelium being destroyed by using the method.

Fig. 1

 1 Fig.1 Three band image of the acquired color image

Fig. 3

 3 Fig.3 the raster count of the two pixel value in the AttributeTable of the ArcGIS

Tab. 1 .

 1 Pleurotus eryngii mycelium growth at different times time/was each day's means of two treatments. Different letters indicate statistically different values (ANOVA/LSD) (P<0.05).

Fig. 4 .

 4 Fig. 4. Growth of Pleurotus eryngii mycelium incubated at different temperatures and relative humidity. A.temperature treatment. B.Humidity treatment.

The research was supported by the project of the Science and Technology Commission of Shanghai Municipality, China (grant No. 08DZ2210600 and No. 08QA14058) and the National Natural Science Foundation of China (grant No. 30800765).


Peng Wan
email: wanpeng09@mail.hzau.edu.cn

Changjiang Long

An Inspection Method of Rice Milling Degree Based on Machine Vision and Gray-Gradient Co-occurrence Matrix

Keywords: Milling degree, Machine vision, Gray-gradient co-occurrence matrix, Fisher discriminance, Rice

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The rice process precision is the embryo extent of the processed rice, for the other saying is how much the embryo of the brown rice have been removed, referring to brown rice is milled to the extent of the cortex, at the same time that is the appearance quality of rice and the main indicator advantages and disadvantages of the performance evaluation of rice quality. According to the rules of "GB 1354-2009 rice," the process precision of rice by machining accuracy can be divided into one, two, three and four levels. There are several means to evaluate the processing, the national standard to provide for the direct comparison method or staining for detection, were evaluated through artificial sense of concept, but these methods are affected by light conditions, subjective feelings or many other factors and the accuracy is not high. The complicated operation process, low detection efficiency, and low accuracy can not meet the rapid, objective, accurate detection needs [START_REF] Xingyi | Progress in research of detection method for degree of rice milling[END_REF] .

Detected by image analysis of rice processing is a very popular research direction [2- 4] . Different levels of precision processing make the surface texture of the rice different. This paper uses machine vision technology to study the surface texture analysis and explore the relationship between precision of rice correspondence and characteristics of surface texture, in order to achieve accuracy by processing rice through testing the surface texture of rice.

There are several means of image texture analysis [START_REF] Boyu | Study of image texture analysis and classification method[END_REF][START_REF] Wen | Image texture analysis methods and recent advances[END_REF] , this paper use gray-gradient co-occurrence matrix to analysis the surface texture characteristics of rice.

Gray-gradient co-occurrence matrix detection method

Gray-gradient co-occurrence matrix embodies the relationship between gray and gradient of each pixel. Gray of each pixel constitute the basis of an image, and gradient constitute the elements edges of the image which provide the main image information [START_REF] Jiguang | Gray level-gradient co-occurrence matrix texture snalysis method[J][END_REF] . Therefore, the gray-gradient co-occurrence matrix considers the Joint Statistical Distribution of the gray and the size of the edge gradient of each pixel.

The operational processes of how to extract the gray-gradient co-occurrence matrix from the image is shown in Figure 1。

Rice image f(i,j)

Obtain the gradient image g(i,j) by the 3x3 sobel operator processing

Obtain the gray-gradient co-occurrence matrix H(i,j)

Obtain the gradient matrix G(i,j) by the regularization processing max ( ) ( ) 1

s g i j L G i j g   , ,
Obtain the gray matrix F(i,j) by the regularization processing max ( ) ( ) 1

g f i j L F i j f   , ,
Obtain the gray-gradient co-occurrence matrix by the regularization processing to the matrix H(i,j) ( ) ˆ( )

x y H i j H i j L L   , ,   j i H , ˆ Fig. 1.
The operational processes of how to extract the gray-gradient co-occurrence matrix from the image Gray-gradient co-occurrence matrix describes the distribution for the gray and gradient of each pixel in an image and the spatial relations between pixels. It is the complex of the gray information and the gradient information, for the reason that we can extract the characteristic parameters of each image from the gray-gradient cooccurrence matrix.

This paper differently collects 15 texture index from the images of the rice grains, such as small gradient strengths(R1), large gradient strengths (R2), gray uneven representation(R3), gradient uneven representation(R4), energy(R5), gray mean(R6), gradient mean(R7), gray mean square(R8), gradient mean square(R9), relevance(R10), gray entropy(R11), gradient entropy(R12), mixing entropy(R13), inertia(R14), inverse gap(R15).

3

The detection of rice processing accuracy

Preparation of the samples

We selected the Wan Chang Rice which was produced in Jilin Province as the samples for the rules research of detection accuracy of the rice. Three rice samples were weight, each 200g. First, the experimental husker was used to shell the paddy into brown rice; then, the brown rice was milled by the milling machine, and collected the rice samples with different precision. As the milling process, the longer you milled, the more brown rice to be grinded, and the different precision you could get. We set three different milling time as 30s, 60s, 90s to milling the paddy, and get three different brown rice as S1, S2, S3. At the same time, we put the Supermarket Wan Chang rice as S4 for the detection accuracy for rice processing test.

The acquisition of rice image

In order to obtain rice samples images, we designed a vision inspection system for rice precision. When detecting, put the rice samples into the hopper, then the V-groove vibrated after the motor driven and made the rice samples fall into the V-groove and ranked to move forward, when the rice grain in V-groove fallen into the drop tank it would accelerate the decline. The transmission worked when the count sensor fallen on the conveyor belt, and the count sensor put the information into the computer; When the rice grains through the camera lens, the corn grain shape detection software collected all the images and sent these images to the computer for detection.

This paper used a microscope to MOTIC stereomicroscope; camera produced in Japan SONY DXC-390P 3 CCD Color camera; image acquisition card produced by the Canadian CronosPlus image acquisition card; conveyor belt for the light blue canvas belt; circular fluorescent light tubes with 5W; major rice-shaped detection software written using Visual C + +6.0.

Rice images process

The operation process of the obtained rice images was shown in Figure 3. From the 4 different rice samples, each sample was selected 300 full grain rice, to get 1200 different images, and preprocess those images to get the original image of rice with different precision.

(1)

(2) From the images, the rice of different precision, its surface texture patterns significantly different. There-fore, we could use the gray-gradient co-occurrence matrix to test the precision of rice. Preprocess to the original image of the rice samples, we get gray-gradient co-occurrence matrix of the rice image, then use the image to collect the texture data, each sample get 300 sets of data.

(3) (4)
200 sets of data were selected from each texture feature data of rice samples as the test samples, and the other 100 sets data as the testing samples. 1,2,3,4 were used as the numerical classification number of rice samples of S1, S2, S3, and S4.

Detection by stepwise discriminant analysis of rice processing accuracy

Discriminant analysis [START_REF] Chong | Fisher discrimination method and its application[END_REF] is based on the known samples to build a discriminant function group, To make the Makes the minimum rate of wrongful convictions in the identification of the sample classification. Analysis variables by the contribution of independent variable, and Introduction and removing variables one by one, until no new significant effects of independent variables could be neither introduced, nor no significant effect of the independent variables are removed from the equation so far.

Adopt the discriminant analysis of group F by the smallest value than the maximum choice of variables. Select when the F value is greater than 7.68 will join the variable into the discriminant function and when the F value is less than 1.355 will remove the variable from the discriminant function as the criterion, Using SPSS software testing 800 samples to analyze the data set of texture features, Calculation the Fisher discriminant function which can be used for identification and classification for unknown samples, Fisher discriminant function is available as Table 2 shows the coefficient。 We can see from table 3, F satisfy the stepwise discriminant analysis into the discriminant function value greater than 7.68 and F value is less than 1.355 , when the variables for the discriminant function to remove a small gradient strengths(R1), representation of uneven gray(R3), energy(R5), mean of gray(R6), mean of gradient(R7), MES of gray(R8), MES of gradient(R10), gray entropy(R11), gradient entropy (R12), gray entropy(R13) 10 texture parameters, etc.. Other variables did not meet the criteria for variable tick was removed from discriminant function.

According to Table 3, construction of Fisher discriminant function groups were as follows:

Q R R R R R R R R R R            (1
Q R R R R R R R R R R           
(2)

Q R R R R R R R R R R            (3
Q R R R R R R R R R R            (4)
Formula( 1), ( 2), (3), (4) form fisher discriminant function group, the discriminant function groups were used in the category test for the precision of the unknown rice samples, put the data of the rice texture characteristics into the 4 discriminant function, and get the 4 discriminant function value, which one got the largest discriminant function value it is the most precision one 。 According to 4 different rice precision test sample images, use gray-gradient cooccurrence matrix to collect texture parameters of rice and use Fisher discriminant function to test classification accuracy of the precision of rice sample, accuracy as shown in Table 3. Prompt: The data underlined detect the correct samples of rice grains From the table 3, using Fisher discriminant function to test the 4 different precision of rice samples, for class S1, the accuracy of detection for rice samples was 93.00%, and 95.00% for class S2, 92.00% for class S3, the class S4 get the highest accuracy of 96.00%. The average accuracy rate of the 4 different precision process is 94.00%.

Conclusions

This paper designed a vision inspection system for rice precision testing to get the image of rice sample, and the texture feature parameters of the different precision rice samples were collected by using gray-gradient co-occurrence matrix, and then constructed Fisher discriminant groups to detect the rice precision. The results show that 400 rice samples of 4 different process precision can reach the average correct to 94.00%; The accuracy of rice samples for processing 30S (the S1 class) was 93.00%, for processing 60S (the S2 class) was 95.00%, for processing 90S (the S3 class) was 92.00%, the milled rice (the S4 class) could get the highest rate of 96.00%. So, this method can be used to detect the rice process precision.

Fig. 2 .

 2 Fig. 2. Rice precision machine vision inspection system. 1.hopper 2.V-groove 3.electric motors 4.drop tank 5.count sensor 6.conveyor 7.Ring light source 8.shot 9.stereomicroscope 10.ccd camera 11.image acquisition card 12.computer software

Fig. 3 .

 3 Fig. 3. Rice image preprocess procedure

Fig. 4 .

 4 Fig. 4. The original image of rice samples of different precision. (1) Rice sample S1; (2) Rice sample S2; (3) Rice sample S3; (4) Rice sample S4

Table 1 .

 1 The parameter mean of the rice samples texture

	Texture parameters	S1	Number of rice samples S2 S3	S4
	R1	0.2398	0.2407	0.2411	0.2346
	R2	6.4022	6.1596	6.0700	6.0915
	R3	0.3990	0.3947	0.3876	0.3509
	R4	0.8339	0.8434	0.8484	0.7790
	R5	0.3365	0.3371	0.3323	0.2813
	R6	10.6028	11.0306	11.0789	11.1660
	R7	1.6924	1.6712	1.6668	1.7152
	R8	4.1985	4.2574	4.1795	3.9872
	R9	2.2332	2.1884	2.1677	2.1366
	R10	0.0582	0.0406	0.0474	0.0369

Table 2 .

 2 The coefficients of the Fisher discriminant function

	Texture parameters	S1	S2	Classification S3	S4
	R1	26456.25	26448.55	26467.79	26506.75
	R3	526.18	517.53		514.82	519.44
	R5	-425.70	-420.82	-420.89	-423.64
	R6	11.07	11.20		11.32	11.37
	R7	1173.44	1174.01	1176.92	1179.87
	R8	3.27	2.95		2.67	2.63
	R9	-269.99	-270.05	-270.69	-271.51
	R11	520.78	517.03		517.03	522.35
	R12	1260.50	1259.41	1259.93	1264.28
	R13	-425.09	-423.56	-424.55	-428.64
	constant	-3965.38	-3960.24	-3965.50	-3980.36

  )

	2	26448.55	1	517.53	3	420.82	5	11.20	6
		1174.01	7	2.95	8	270.05	9		517.03	11
		1259.41	12	423.56	13	3960.24

  )

	4	26506.75	1	519.44	3	423.64	5	11.37	6
		1179.87	7	2.63	8	271.51	9	522.35	11
		1264.28	12	428.64	13	3980.36

Table 3 .

 3 Classification of the test samples of rice processing precision

	samples	S1	the forecast of rice sample precision S2 S3	S4	total
	S1	93	7	0	0	100
	S2	3	95	2	0	100
	S3	0	5	92	3	100
	S4	0	0	4	96	100
	correct rate/% average correct rate/%	93.00	95.00	92.00 94.00%	96.00	-


Lihua Jiang
email: jianglh@caas.net.cn

Wensheng Wang

Xiaorong Yang

Nengfu Xie
email: nf.xie@caas.net.cn

Youping Cheng
email: youping@caas.net.cn

Classification Methods of Remote Sensing Image Based on Decision Tree Technologies

Keywords: Decision tree, Classification, Remote sensing image

Decision tree classification algorithms have significant potential for remote sensing data classification. This paper advances to adopt decision tree technologies to classify remote sensing images. First, this paper discussed the algorithms structure and the algorithms theory of decision tree. Second, C4.5 basic theory and boosting technology are explained. The decision tree technologies have several advantages for remote sensing application by virtue of their relatively simple, explicit and intuitive classification structure.

Introduction

Classification and Extraction of remote sensing information has been an important content in remote sensing technology field. In remote sensing classification application, traditional classification methods [START_REF] Li | Decision Tree Classify Method and Application in Earth Coverage Classify[END_REF] such as supervised classification and unsupervised classification and artificial neural nets classification [START_REF] Luo | Study and Implement of Remote Sensing Image Decision Tree Classifier[END_REF] and expert system classification are both based on spectral image features. But because image self has the phenomenon that the same thing has different spectrum, and different things have the same spectrum, the classification methods that only rely on ground spectrum features always turn up many misclassifications and omission errors [START_REF] Li | Remote Sensing Image Auto Classify Study Based on Combination of Artificial Neural Networks and Decision Tree[END_REF]. Lots of study indicates that classifications combined with image spectrum information and other assistant information can improve precision of classification largely. Decision tree classification as spatial data mining and knowledge discovery [START_REF] Jiang | Use Texture Analysis to Extract TM Image Information[END_REF] supervised classification method, breaks through the problem that construction of previous classification tree or classification rule always take advantage of ecology and remote sensing knowledge ex-ante certainty and the results always closely related with experience and professional knowledge [START_REF] Friedl | Maximizing land Cover Classification Accuracies Produced by Decision Trees at Continental to Global Scales[END_REF].

Decision tree arithmetic

Decision tree is a method which can inductive learn by training samples and build up decision tree or decision rule and then use decision tree or decision rule to classify data. Decision tree is a tree construction. It is composed of a root node, a series of internal nodes and leaf nodes. Every node can have only one father node and two or more child nodes. Nodes are connected with each other by branches. Every internal node correspond a test properties or properties group and every side correspond every possible value of property. Leaf node correspond a class property value and different leaf node can correspond the same class property value. Decision tree can not only be expressed by tree, but also a team of IF-THEN production rules. Every road from root to leaf correspond one rule and the condition of rule is to option of all nodes property values. And result of rule is class property of leaf node in the road. Compared with decision properties, rules are more simple and convenient to understand, use and mend and can make up the base of expert system. So rules are used more and more in actual application. This paper mainly introduces a widely used in remote sending application arithmetic-classify and regression tree and another decision tree arithmetic C4.5.

Classify and Regression Tree (CART)

Classification and regression tree is a common tree growth algorithm. It is presented by Breiman etc. [START_REF] Li | Remote Sensing Image Classify Method Study Based on Decision Tree[END_REF] and it is a supervised classify. It trains the samplings to construct binary tree and decode to classify. The feature is to take advantage of the binary treestructured fully, in other words, root node includes all samplings. Root node is divided up into two child node in definite divide rules. This process repeats again in child node and becomes a regression progress until the child node can not be divided into two child nodes. Train of thought of construction a CART is that based on the whole sampling data, build up a multilevel and multi-leaf nodes tree to reflect relations between nodes and then cut the tree to build up a series of child trees and select appropriate tree in order to classify the data. In details, the process includes building up a tree and pruning a tree.

Tree growth.

A discrimination of tree nodes is named a branch and it corresponds to a subset of training samples. Branch of root node corresponds to the whole training samplings.

Thereafter discrimination is process of partition training samplings. So process of building up a tree is querying property to produce partition rules. In this paper, CART adopts a index called "node impurity level": i(N) presents the impurity level of node N. When mode data in node cone from the same category, i(N)=0; when categories of data distributes evenly, i(N) will be very big. Partition rules are produces based on minimal value of impurity level function. Here two impurity functions are introduced.

(1)"Entropy Impurity",is also called information impurity:

( ) (
)

) ( log 2 j j j w P w P N i ∑ - = (1)
Including, ( )

j w P
is the calculus of probability accounting for j w mode sampling data that node N belonged to of the whole samplings. According to the characters of entropy, if all mode data come from the same category, the impurity level is zero; or else impurity level is more than zero; when all categories data appears with the same calculus of probability, entropy is the maximum.

(2)VAR Impurity-"Gini impurity level".According to node samplings come from different categories and it is related with total distribution variance, below formula is put forward.

( )

( ) ( ) ( ) ∑ ∑ - = - = ≠ j j j j i i w P w P w P N i 2 1
(2) Meaning of "Gini impurity level" is to represent error rate of category making in node N. When given a tree which has grown to node N and the node is attribute queried, a visible heuristic train of thought is to select the query whose impurity level drops fastest. The impurity level drops can be noted: P is the probability that when query T is adopted, the tree grows from N to L N . And the optimum query value S is the maximum value of ( )

( ) ( ) ( ) ( ) ( ) R L L L N i P N i P N i N i - - - = ∆ 1 (
T i ∆ .

Tree pruning

If we persist in building up trees until all the leaf nodes reach minimal impurity level. The data will be fitted excessively and classify tree will degenerate to a convenient lookup table. It is maybe not good for noise signal lamp generalization performance of bigger Bayes error. On the contrary, if the branches stop too early, error of training sampling will be not small enough resulted in category performance is very poor. A main stopping branch method is pruning, at the same can prevent the tree growing too gigantic. In this paper, below index is adopted to reach the aim.

( ) According to formula (4), tree pruning can be completed by below two steps: [START_REF] Li | Decision Tree Classify Method and Application in Earth Coverage Classify[END_REF]In all the brother leaf nodes, compare t cos after combined leaf nodes.

∑ + • = node leaf cos n i size t α (4)
(2)Delete the leaf node that t cos reduces the most. If t cos has not reduced, nothing will be done. Repeat above pruning process until pruning can not go on. In pruning process, training error deduces with leaf nodes increasing; testing error deduces at the beginning and reaches minimum and then gradual roll up affected by training samplings. Take use of independent data to test, and select the subtree which has the minimum test error as decision tree. This paper adopts a heuristics verification technique -cross validation to select the best tree: 10-fold cross validation. The training samplings are divided into ten subsets which are equality in number and disjoint with each other. Classifier will train the data 10 times and every time nine groups data subsets are trained and the test one is as validation set to estimate testing error. Estimated testing error is the average value of the ten groups.

C4.5 basic theory

C4.5 is another widely applied signal decision tree building up method, and is adopts information gain ratio to classifier. It uses training group to select the properties whose information capture rate is the largest and information gain in not less than all properties average value as tree nodes. Take every possible value as a branch of node and recursively builds up a decision tree. Entropy impurity level function in CART is adopted in building up a tree. The information gain is equivalent to "impurity level decreasing value" in CART. In addition, index of capture rate is added in order to wipe off influence of high branch property. At the same time, capture rate take leaf node count and size of every node after every partition into account. Consideration objects mainly are every partition but not information content in category.

Termination condition is that properties of records in subset are the same or no property can be divided. The difference compared with CART, C4.5 take advantage of statistical significant error probability technique based on branches to realize pruning. Another significant difference is that processing method to damage pattern. In training period, C4.5 has not adopted surrogate split to settle damage of categorization data, but adopts probability weighting method to deal with "property missing".

A new technique adopted in decision tree-boosting method

In decision tree classifier designing, a boosting technology is widely used in the middle period of 1990s in machine learning field to improve classifier precision.

Conclusion

The advantage of decision tree algorithm used in remote sensing data classify lies in that it can show the shortage of MLC algorithm when deals with complicated distribution data sets. Decision tree has better flexibility and robustness for data distribution feature and classify marking. So when remote sensing image data features distribution is very complicated or dimensions of source data have different statistical distribution and scales, decision tree classify method can obtain the best classified results. Tree classify construction of decision classify method need not suppose some sort of parametric density distribution in advance. So the whole classify precision is superior to traditional parametric statistics classify method. But with the development of artificial intelligence technology and theory, study of remote sensing image classify has developed to a higher level. Geonomy knowledge and aid decision making of geographic information can boost precision of remote sensing image classification and information extraction and expert system is a good means to resolve this problem. So combination of decision tree and expert system based on knowledge is becoming a cause for concern.

N

  are separately left node and right node; ( )

t

  cos is represented cost function of tree weighting error probability and complexity penalty summation. size is represented leaf node quantity to weight complexity of tree classifier. α is represented complexity index. of impurity level of all of leaf nodes to show the uncertainty of adopting this classification tree to classify training samplings.

  This method can boost samplings classifier precision which is difficult to recognize. At the same time, this technology can cut down sensitivity that classifier algorithm affecting data noise and training sampling error. Boosting is a learning method which can boost any learning algorithm precision and it can boost weak learn algorithm to strong learn algorithm. Its theory comes from probably approximately correct learning model. It can take advantage of some learning algorithm to generate a series of base classifiers. Every base classify training depends on classifier results produced by former classifier and endows failed training samplings with major weight to pay them more attention in subsequent learning. At last, classifier weights voting every base classifier and gets the last result and reduces signal classifier error and improve classify precision. Freund and Schapire brought forward the most pragmatic boosting algorithm-AdaBoost according to boosting basic theory in 1995 and widely applied.

Acknowledgements

This work is supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), Special fund project for Basic Science Research Business Fee, AIIS(Grant No. 2010-J).


Xiaorong Yang

Wensheng Wang

Qingtian Zeng

Nengfu Xie

An Intelligent Retrieval Platform for Distributional Agriculture Science and Technology Data

Keywords: Agriculture science and technology data, Data integration, Agriculture ontology, Intelligent retrieval

In the agricultural domain, the variety of data used by organizations is increasing rapidly. Also, there is an increasing demand for accessing these data. Now, the problem of the digital divide causes serious problems in manipulating the distributed information. Based on this condition, this paper presents the intelligent retrieval architecture of distributional agriculture science and technology data which focuses on research of the integration support technology, the concept extending retrial technology based on agricultural ontology and the personalization retrieval technique based on the user model. In the experiment, the intelligent data application platform provided by the paper proves that the architecture is effective.

Introduction

With the development of computer and network technology, the amount of data which are collected, saved, processed and transmitted has grown rapidly. Many sharing and serving platforms of agriculture science and technology information are constructed by different departments throughout the country. But these platforms lack a unified plan and management in the important implementation techniques and storage technology. The heterogeneity and dynamic distribution become basic features of these systems at present. Particularly the heterogeneity in semantics results in data sharing difficulty. An intelligent data application platform should be constructed to make full use of different distributed heterogeneous data resources. The platform can provide a public and unified data access interface of different distributed data sources for users. Users needn't consider the problem of data extracting and data combining.

So the unified and high-efficiency access of data can be achieved. The management and retrieval of data sources layer consists of the node metadata management module and the web retrieval module. The node metadata management module manages bottom database sources and the web retrieval module can accept query parameters from upper layer, access database and return retrieval results.

The

The central metadata mapping and intelligent retrieval layer consists of the intelligent retrieval module, the central metadata mapping database and the central metadata manager. The intelligent retrieval module can accept query parameters from system interface. According to the central metadata mapping table it can find corresponding data source and submit this query parameters to corresponding web retrieval module.

The central metadata manager can manage metadata and mapping relation between metadata and data sources.

The system interface layer provides classification retrieval and keywords retrieval for users. The platform completes semantic extension for query condition which a user inputs and submits them to the bottom web retrieval module.

The Concept Expansion Retrieval Technology Based on Domain Ontology

Domain ontology technology is adopted to standardize retrieval keywords in order to reach united comprehension to information between human and human or Machine. This study constructs ontology by using the graphical interfaces of the ontology edit tool Protégé. According to ontology rules, node administrators extend the keywords in the semantic dictionary from four properties of synonym, abbreviation, English language and Chinese pinyin. Thus a unified semantic dictionary is set up in the center database and becomes more and more abundant (Song [START_REF] Lan | A Study of Intelligent Semantic Information Processing System Based on Ontology[END_REF]). It can improve the recall ratio of information retrieval. Then the center administrators delete repetitive and ambiguous words. The precision ratio of the platform can be implemented (Chen Lihua. 2010).

By semantic analysis of ontology concept and retrieval keywords, retrieval association and expansion are completed step by step. This technology supports synonym retrieval, information retrieval of different languages and recommendation of related information resources. For example, if a user inputs the keyword crop, Chinese and English information about crop can be searched and information about rice and wheat can be searched.

The Personalized Recommending Technology Based on User Model

User interest model is set up according to user's explicit demand and implicit demand.

It maintains uses' history behavior information and personal information. It provides different comprehension of same keywords from different users in depth and scope.

User feedback process based on users' opinions makes retrieval service more accurate and friendly. User interest model can analyse a user's behavior and record and mine users' hidden interest. Because the user's interest changes, user interest model self-studies continuously to improve itself (Fei Hongxiao et al. 2009). The platform sets higher priority to the information which are often accessed by the user. user interest model can forecast a user's interest and demand to implement personal information retrieval and recommending. Firstly, this study implements the dynamic sort of information resources according to a user's interest. When a user accesses some information resources, the system records his behavior and analyses his interest in classified information resources. When the user retrieves information again, the data resources which are often accessed by him will be displayed ahead. Secondly, the system can customize personal fields of database. The fields can be defined as the language and words needed by a user in order to satisfy his usage pattern. Finally, the system can record information accessed by a user. The user can operate the accessed records and define if they are useful to him. By calculating the probability of the accessed information, the user's interest in information resources of some sort can be gotten.

Application Case

To evaluate the intelligent retrieval platform for distributional agriculture science and technology data, the platform is applied in the management of Tibet science and technology information resource. In Tibet, all kinds of information resources are saved in different database and websites. These systems don't communicate each other because of the independence in the design and deployment. By applying the intelligent retrieval technology, the platform integrates, maintains and shares the distributional agriculture science and technology data in Tibet. The platform provides a unified data access interface for distributed heterogeneous data sources. Through the interface users access the needed information conveniently and needn't consider the problem of data extracting and data combining. Not only the information which meets the inputted keywords can be searched, but also the information about the synonym, English language and related information of the inputted keywords can be found. And the information are displayed according to users' interest priority. The retrieval intelligence and individualized service of the platform satisfy users' demand.

Conclusion

To integrate and share distributional heterogeneous agriculture science and technology data, this study designs and implements the intelligent retrieval platform for distributional agriculture science and technology data. This paper introduces the logic and function architecture of the platform and the integration supporting technology of distributional agriculture science and technology data, the concept expansion retrieval technology based on domain ontology and the personalized recommending technology based on user model. Finally, as an application case, the platform has been applied to manage Tibet science and technology information resource to verify the performance of the management platform.

  Architecture of the Intelligent Retrieval Platform of Distributional Agriculture Science and Technology Data 2.1 The Logical Architecture of the Intelligent Retrieval Platform of Distributional Agriculture Science and Technology Data A traditional retrieval system of distributional agriculture science and technology data includes distributional data integration module, data category module and data retrieval module. To improve retrieval intelligence and satisfy users' individualized need, the intelligent retrieval module and personalization service module are designed based on the traditional retrieval architecture of distributional agriculture science and technology data. The intelligent retrieval module uses domain ontology to support the information retrieval based on different languages, synonyms and related information resources. The personalization service module can record and mine users' historical data to discovery users' interest. It can recommend information according to users' interest. Fig 1 shows the logical architecture of the intelligent retrieval platform of distributional agriculture science and technology data.

Fig 1 :

 1 Fig 1: Intelligent Retrieval Logical Architecture of Distributional Agriculture Science and Technology Data

Fig 2 :

 2 Fig 2: Detailed Function Architecture of the Intelligent Retrieval Platform of Distributional Agriculture Science and Technology Data

Fig 3 :

 3 Fig 3: Logical Architecture of Integration and Category of

  The concept-based retrieval technology can improve retrieval efficiency and speed. This study adopts description logic to establish domain ontology model and analyses the reasons of the heterogeneity among information systems from the angle of ontology. The semantic integration framework of the heterogeneous systems is established based on ontology mapping. Domain ontology can be set up in two methods (Cao Yukun et al. 2010). Semantic extension keywords set and some metadata set of node data sources inputted by node administrators are main domain ontology keywords. And the domain keywords extracted from the specialized websites are ancillary sources.

Acknowledgements

The work is supported by the Academy of Science and Technology for Development fund project "intelligent search-based Tibet science & technology information resource sharing technology", the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2009ZX03001-019-01), and the special fund project for Basic Science Research Business Fee, AII (No. 2010-J-07).


Chaojie Jia
email: jiachaoj280@126..com

Wenlong Zhao

Yaxiong Chen

Guojun Sun

Delineation of Suitable Areas for Maize in China and Evaluation of Application for the Technique of Whole Plastic-Film Mulching on Double Ridges

Keywords:

Climate, topography, soil and land use data which closely associated with the distribution of maize (Zea mays L.) has been collected and collated to identify the suitable areas for maize on condition of nature and using the technique of whole plastic-film Mulching on Double Ridges in China, the database of these factors have been created in the current article. The weights of these factors, criteria and suitability levels have been defined using Multi-Criteria Evaluation (MCE) approach based on Geographic Information System (GIS), each factor was classed into five suitability levels: Most suitability, Moderate suitability, and medium, Moderate unsuitability and Unsuitability. Pair-wise comparison matrixes were made to get the weights of each factor, the suitability maps of the factors have been obtained by overlaying these layers according to their weights, got the suitability map for maize. The suitable areas map was created by the suitability map and the land use map which has been masked the non-cultivated land. The result indicated that the higher yield of maize can be gained from 4.535×10 5 km 2 which distributed in 741 counties in16 provinces. In accordance with the method of above, analysis the suitable area after the techniques of whole plastic-film mulching on double ridges was used. The results showed the higher yield could be got in 6.145×10 5 km 2 , some areas of Gansu, Inner Mongolia and other places in arid and semiarid areas that can't distribute or only have low yield maize could get higher yield after using the technique of whole plastic-film mulching on double ridges. With the increased requirement of food, the new techniques and the more perfect regional planning of grain could be take into account for socio-political and environment issues.

Introduction

Protected cultivation, mainly represented by plastic-film mulching, has greatly improved crop production worldwide since the 1950s [START_REF] Anikwe | Tillage and plastic mulch effects on soil properties and growth and yield of cocoyam (Colocasia esculenta) on an ultisol in southeastern Nigeria[END_REF]. However, the technique of whole plastic-film mulching on double ridges is trialed in China from the 1990s, and is in phase of field test and promotion currently. The technique can reduce water loss through evaporation, therefore may increase water available to plants, and increase the topsoil temperature [START_REF] Li | Different conservation tillage related with soil water. Soil Temperature and Yield of Potato in Rainfed Farming System[END_REF]. It could be applied in the areas that have low temperature or rainfall to improve the grain yield.

To research and develop agriculture on condition of using the technique of whole plastic-film mulching on double ridges, the accurate identification and the characterization of current production areas and potential areas are necessary [START_REF] Corbett | Dynamic crop environment classification using interpolated climate surfaces[END_REF]. MCE approach was used to determine relevant criteria (factors) which is understanding as the biophysical restraints and defined the suitability levels for each factors. MCE was defined as "an umbrella term to describe a collection of formal approaches which seek to take explicit account of multiple criteria in helping individuals or groups explore decisions that matter [START_REF] Belton | Multiple Criteria Decision Analysis. An Integrated Approach[END_REF], could be understood as a world of concepts, approaches, models and methods that aid an evaluation (expressed by weights, values or intensities of preference) according to several criteria [START_REF] Barredo | Systems Information Geography evaluation multi-criterion ordination delterritorio[END_REF]. MCE has been one of the most widely applied models in management and planning because of it was (1) the formal approach, (2) the presence of multiple criteria and (3)the evaluation are made either by individuals or groups [START_REF] Mendoza | Combining participatory modelling and multi-criteria analysis for community-based forest management[END_REF]. Geographic information system (GIS) has a powerful function in spatial analyses such as: predicting the distribution of the wild relatives of bean by analyzing climate conditions that favor bean's growth, and for planning potential conservation areas by using relationships between environmental factors and the distribution of birds. However, the utility or GIS functionality in the management of the above areas has been limited by the restrictions inherent in overlaying of digital information maps. Some of these restrictions are: [START_REF] Anikwe | Tillage and plastic mulch effects on soil properties and growth and yield of cocoyam (Colocasia esculenta) on an ultisol in southeastern Nigeria[END_REF] overlays are difficult to use when there are many underlying variables (more than 4), (2) the overlay procedure does not enable one to take into account that the underlying variables are not of equal importance [START_REF] Janssen | Multicriteria analysis and GIS: an application to agriculture landuse in the Netherlands[END_REF]. From the 1990s, integration of the MCE approach with GIS for solving spatial planning problems has received considerable attentions among urban planners. The ability of GIS to integrate with the MCE approach has been shown in studies related to site determination for a nuclear waste facility and for a noxious waste facility. And the GIS-based MCE has also extended to solving planning problems that involve conflicting multi-objectives such as land use allocation problems [START_REF] Janssen | Multicriteria analysis and GIS: an application to agriculture landuse in the Netherlands[END_REF][START_REF] Eastman | Raster procedures for multicriteria/multiobjective decisions[END_REF] MCE seems to be applicable to GIS-based land suitability analysis [START_REF] Pereira | A multiple criteria decision-making approach to GIS-based land suitability evaluation[END_REF] and help us to carry out the delineation of suitable areas for crops. The maize is one of the most important grain and forage crops in China, playing a crucial role in protect food security. The main goal of this research is to describe suitable areas for maize on condition of natural and after use the technique of whole plastic-film mulching on double ridges under the GIS based MCE to evaluate the application area for the technique of whole plastic-film mulching on double ridges. With the increased requirement of food, the new techniques and the more perfect regional planning of grain could be take into account for socio-political and environment issues.

Methods

Study areas

The study area throughout the land of China, include mainland of China, Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan province. It is located latitude from 3°52′N to 53°33′N and longitude from 73°40′E to 135°2′30″E. The total area is approximately 9600000km 2 .

Fig1. The study areas

2.2Procedures

The distribution and growth of crops are decided by water, temperature, light and soil. According to the: leading dominant (the main factor affecting the growth of maize); incompatibility (the factor affect the maize independence); diversity (selected factors were significantly different, can express the threshold); marketability (factors which be chosen have a corresponding data) [START_REF] Shi | The weighted method for the score that experts give[END_REF] In the light of expert opinions, as well as literature, eight factors which are closely connected with the growth of maize were selected to establish the relevant criteria of MCE. They were: accumulated temperature, average temperature in April which was regarded as the Minimum temperature, the average temperature in July was the Maximum temperature, the annual precipitation, Soil Texture, Soil pH, Elevation and Field water-holding capacity. Then, the database was established.

Fig2. The flow chart shows the main procedures applied in this study 2.2.1 Establish of spatial databases 1. Climate Database Climate information was obtained from 722 meteorological stations which belong to the China Meteorological Administration and located within or close to the study area. The recorded years were from 1978 to 2008. The format of original data which we obtained was text, calculated in the EXCEL; Based on these data, Accumulated temperature (≥ 10 ℃), the Minimum temperature, the Maximum temperature and the Precipitation was extracted and calculated separately. Convert the data to point in ArcGIS, and then interpolate the temperature data and the precipitation data with the Kriging and the IDW method. Before interpolation for the spatial data, compare the accuracy of various interpolation are necessary. 90% of the points were used as the training points and the other were test points, using mean relative error (MRE) between the measured (Zoi) and the value after interpolation to test the accuracy of the inverse distance weighting (IDW), ordinary kriging(OK), spline(SP) and trend surface (TR). Formula as follows:

( 1) The results showed that OK was the best interpolation for the temperature.

Table1.

Soil Database

Soil characteristics data were taken from digital Soil Type Maps (from ISSCAS) using a scale of 1:1 000 000. Sampling points was created, the total number of points were 99034. The information of soil texture and soil pH was obtained from soil type. Then the soil texture point data and the soil pH point data were interpolated into grid maps within ArcGIS.

Relief Database

The altitude data were obtained from the digital elevation model (DEM). DEM has characteristics of space location and attribute of terrain, it is an indispensability part when establish resource and environment information in different levels. National digital contour map at the scale of 1:250,000was obtained from the State Bureau of Surveying and Mapping. This contour data was used to create DEM within ArcGIS, the process was contours → TIN → lattice → DEM.

Unification of the data format

All the data was converted to the same format, the spatial resolution was 1000 m per pixel, the Krasovsky_1940_Albers coordinate system was used as the projected coordinate system and GCS_Krasovsky_1940 was the geographic coordinate system. The spheroid of the maps was the Krasovsky_1940 and the datum was D_Krasovsky_1940.

MCE process for suitable areas of maize 1. Define specific suitability level of the factors

By means of expert opinion and literatures, a specific suitability level per factor for maize on condition of natural and using the whole plastic-film mulching on double ridges technical was defined (Table 2). These levels were used as a base to construct the criteria maps. The levels were: Most suitable, Moderate suitable, Medium, Moderate unsuitable, unsuitable. Then factor maps were constructed in the ArcGIS environment. On condition of using the technique of whole plastic-film mulching on double ridges the threshold of restriction of precipitation was decreased for 50mm [START_REF] Zhao | Analysis of gray association between soil water with rainfall under the full coverage double-ridge with plastic and water utilization rate for maize[END_REF], and the accumulated temperature restriction decreased for 650°C [START_REF] Zhao | The advantage and The application of the technique of whole plastic-film mulching on double ridges[END_REF] ,the restriction condition of Min temperature decreased 2°C and elevation reduced for 300m, the field water-holding capacity decreased for 0.10 [START_REF] Ma | The research for the water-saving technology of the whole plastic-film mulching on double ridges for maize[END_REF].

Table2. Level of suitability for maize on condition of nature Saaty (1977) in the context of a decision-making Process known as the Analytical Hierarchy Process [START_REF] Eastman | Raster procedures for multicriteria/multiobjective decisions[END_REF]. It has characterized of systematization, flexibility and practicality. Relative importance of each factor were compared, according to regression between them and final yield of maize, the score of each factor were given, and amend the score with the relationship between them. The first pair-wise comparison matrix was obtained. Ratings were provided on a nine-point continuous scale, which ranges from 9 to 1/9. A 9 indicates that relative to the column variable, the row variable is significantly more important. A 1/9 indicates that relative to the column variable, the row variable is significantly less important. Statistic the number of each factor appeared in the literature which described the yield and the distribution of maize, establish another Pair-wise comparison matrixes. Test two matrix use the CRJ (Matrix Consistency ratio), if the CRJ<0.10 matrix was available; if the CRJ>0. 10[14], we must amend the matrix on the opinion of experts who research the maize. Weight was calculated in the MATLAB software using the pair-wise comparison matrixes above.

Weighting for the factors A variety of techniques exist for the development of weights, but the promising technique is the pair wise comparison developed by

Table4. Analysis of weight for various factors

Mask

Overlay the factor maps with their weight, and then crossing the land use/cover map which has been taken out of the city, lakes, rivers and other types of land that can't or won't grow the crops with the suitability map to find the plant areas. These works were conducted with the Weighted Overlay Module in the ArcGIS environment.

Results

Fig. 3: factor maps for maize on condition of natural, including suitability levels for each factor. 1= Accumulated temperature, 2= Precipitation, 3=Minimum temperature, 4= Maximum temperature 5= Field waterholding capacity, 6=Soil texture, 7= Soil pH, 8=. Elevation Fig. 4: factor maps for maize on condition of using the whole plastic-film mulching on double ridges technical, including suitability levels for each factor. 1= Accumulated temperature, 2= Precipitation, 3=Minimum temperature, 4= Maximum temperature 5= Field waterholding capacity, 6=Soil texture, 7= Soil pH, 8=. Elevation The technical play a marginal role when maize in the stage of milky [START_REF] Zhao | The advantage and The application of the technique of whole plastic-film mulching on double ridges[END_REF]). The most suitable area of six factor maps was extent after use the technical, especially the Precipitation was the most significant. It means that this technical has a prominent role for the Precipitation. The area that distensible mainly distributed in where the arid and semiarid area or the cold area. The Field water-holding capacity also has the significant change.

Fig5. Maps of suitability for maize in China (1)On condition of nature (2) on condition of using the whole plastic-film mulching on double ridges technical On condition of natural, the area of the most suitable level was 4.53505×105 km2, the moderate suitable level was 2.048032×105 km2. After using the whole plastic-film mulching on double ridges technical, the area of the most suitable level was 6.14456×105 km2 and the moderate suitable level was 2.12719×105 km2. The most and moderate suitable level could got more yield and was considered use the land efficiently.

Fig6. The map of the area congruent for using the whole plastic-film mulching on double ridges Evaluate the suitable level use the same method and then crossing the land use/cover map, the result showed that there are 1.609×105 km2 increased from the other levels to the most suitable levels, that lead the yield of maize increased about 6.29×1010kg. 7.97×104 km2 of the other levers was improved to the moderate suitable level, and that bring on the yield of maize increased about 1.642×1010kg. The area that congruent for using the whole plastic-film mulching on double ridges was located in Inner Mongolia, Gansu, Ningxia, Shanaxi, Shanxi and so on. The precipitation of these province was the restrict condition for developing agriculture. In other areas which could use the technical were mainly because it could improve the temperature of topsoil.

Discussion

Production and distribution of maize was the result of multi-factor effect [START_REF] Li | Light and temperature on maize yield components of value[END_REF], the eight factors which have been chosen in this research were the restrictive factor in various stage of maize growth in the natural conditions. Based on the report of Ceballos-Silva et al, the spatial data input, extraction, analysis and visualization functions of GIS were used to establish the national spatial database of climate, topography and soil. MCE procedure in this research was useful to evaluate the suitable areas for maize. As the first phase of MCE, the factors were selected based on agronomic knowledge of local experts and reviews of existing literature. And then the Pair-wise comparison matrixes in the context of Analytical Hierarchy Process were made to obtain the weights and confirmed to be a useful approach. Finally, five suitability levels of maize were divided using the method of GIS-based MCE crossing this the land use/cover map, calculate the plow land of every levels. After use the technique of whole plastic-film mulching on double ridges, the threshold of some natural condition for restriction of maize decreased and that lead to the areas were extent. The GIS based MCE allowed us use more database of spatial to evaluation the suitable levels of maize as different significance. The weights was obtained in this paper can be used in many similar research of maize. The feedback process of checking the results by local agronomic experts was involved, and results could be adjusted in light of their experience. The results of evaluation for natural condition showed that this method was reliable and reasonable. Expert system based on the research results will be constructed and connected with the internet to facilitate the work of decision-makers and farmers. However, the identified variety-suitable areas were proposed at a theoretical maximum, the microclimate and micro topography of the specific areas should be considered in the actual production. Decision-making process to select adequate crop patterns could be based on other issues such as: production supports (by local and federal governments), marketing, technological level, economic evaluation, in addition to local cultural traditions, which are very important also. This factor could be used in the further research.

Conclusion

In this research, the MCE approach was applied to identify the suitable level and area for maize in the condition of natural and use the technique of whole plastic-film mulching on double ridges within GIS environment. The results confirmed that the methodology used was adequate to construct and integrate spatial databases of climate, soil, topography and land use. The interpolated factor maps of temperature, precipitation, elevation, soil pH, soil texture class and land use information were crucial in identification of suitable areas for maize. The technique was more useful for the arid and semiarid area to increase the yield of grain, especially for the north-west of China. Crop mapping based on the results will be constructed and connected with the internet which facing the government and farmers to adjust agricultural structure according to market conditions.

  The compare of accuracy of various interpolation method

		IDW	OK	SP	TR
	accumulated temperature	0.137713		0.082544	0.159480 0.098259
	Minimum temperature,	0.105527		0.096969	0.115875 0.134711
	Maximum temperature	0.098793		0.055474	0.088144 0.092951

Acknowledgments

This work is financial supported by the ISTCP for Construction of an Information Platform/module in Eco-agricultural Assessment and Management (EAM) (2010DFA31450) and National Science Fund for Talent Training in Basic Science (J0630644).


Peng Wan
email: wanpeng09@mail.hzau.edu.cn

Changjiang Long

Xiaomao Huang
email: huangxiaomao@mail.hzau.edu.cn

Keywords: Process quality, Rice color, BP neural network, Rice

This paper proposed a detection method of rice process quality using the color and BP neural network. A rice process quality detection device based on computer vision technology was designed to get rice image, a circle of the radius R in the abdomen of the rice was determined as a color feature extraction area, and which was divided into five concentric sub-domains by the average area, the average color of each sub-region H was extraction as the color feature values described in the surface process quality of rice, and then the 5 color feature values as input values were imported to the BP neural network to detection the surface process quality of rice. The results show that the average accuracy of this method is 92.50% when it was used to detect 4 types of rice of different process quality.

A Detection Method of Rice Process Quality Based on the Color and BP Neural Network 1 Introduction

Rice is one of the most important crops in the world, the staple food of about half of the world's population is rice. The harvested paddy needs be processed into rice for human consumption by the processes of huller, mill, polishing and so on. The evaluation standards of rice process quality including grain shape, appearance and color, chalky, fragmentation rate, et al. The process quality of rice is one of the most important factors to determine the appearance quality and the selling price of rice. In the process of rice, the process quality of rice often judged by skilled workers, but due to people's subjective factors, it is difficult to describe accurately the results of the process quality of rice.

With the development of science and technology, image analysis technology is widely used to detect and evaluate the rice quality [START_REF] Hou Caiyun | Application of 3D-Microslicing image processing system in rice quality evaluation[END_REF][2][3] [START_REF] Abdullah | The applications of computer vision system and tomogr-aphic radar imaging for assessing physical propert-ies of food[J][END_REF] . The color of rice is one of the main factors of evaluating the quality [START_REF] Shang Yanfen | Automatic detection of yellow-colored rice using image recognition[END_REF] . While detecting the rice quality by the color features, people adopt more RGB color space and HIS color space; in addition, L* a* b* color space is also commonly used to extract the color feature value [START_REF] Cai | An analysis of color models and criteria for their application to quality test of farm products[END_REF] [START_REF] Vizhanyo | Enhancing color differences in images of diseased mushrooms[END_REF] .

Since Rumelhart and others [START_REF] Rumelhart | Learning representations by backpropagation errors[END_REF] proposed the back propagation algorithm, neural networks are widely used in many fields of agriculture. Majumdar S.,D.S. Jayas and others [START_REF] Majumdar | Classifieation of cereal grains using machine vision: Morphology models[END_REF] identified the type of grain by neural network system based according to the characteristics of grain morphology; Kazuhiro Nakano [START_REF] Nakano | Application of neural networks to the color grading of apples[END_REF] used the neural networks to identify the quality of Apple's appearance, and the experiments showd that the BP neural network can classify the apple into three classes by the color features of the apple.

The purpose of this paper is to propose a method of extracting rice color feature values, and detect the process quality of rice according to color feature values and through artificial neural network.

2

Materials and methods

Rice samples

The rice samples were Wan Chang Rice, produced in Changchun, Jilin Province. The samples of rice grain were processed by huller, milling, polishing and other processes, and then respectively obtained the rice samples with 500g of four different forms: brown rice (BR), the first process rice (FR), the second process of rice (SR), polished rice (PR), and then packaged in sealed bags and kept in the shade.

Computer vision system

In order to obtain images of rice grains, the paper designs of the rice quality detection vision system for collection the images of rice samples.

Rice image processing

To extract the color features of rice, rice needs to be extracted from the background image firstly. The process flow chart of a rice image was in Figure 1. The detection flow chart of rice process quality by image analysis was shown in Figure 2.

Color extraction

For extracting the color values of rice image, we must first identify the color region in the abdomen of the rice. In order to guarantee every rice' color extraction region contains the same number of pixels, this paper detected the equal area in the abdomen of the rice.

This paper firstly calculates the cancroid of rice image by image processing, then an extraction region of the color values of rice image (CA, Color Area) is identified, which has the centroid of rice image and a circle of radius R. The extraction region of the color values of rice image (CA) must be in the outline of the rice image.

The pixel coordinates of the rice image are expressed as {(xi, yi) | 0 ≤i≤ M, M is the pixel image points}, and the centroid coordinate of the rice image is (X,Y), then:

0 0 1 1 M i i M i i X x M Y y M              (1)
The color extraction regional diagram of rice image is in Figure 3.

r 5 i i R  (2)
In this paper, the average value of the pixel color values in sub-regional Sa i of the CA is the color value of sub-regional Sa i , and then the color of every rice can be described by 5 color values extracted from the CA.

2.5

Color conversion

The rice images obtained by the CCD are based on RGB color space. HSI color model is based on the human visual, which describe the color by using the Hue(H), Saturation(S) and Intensity(I) to sort. As HSI color mode is related with the hardware features, and is little sensitivity to the light source. This paper describes the rice color by using the HSI color system.

Firstly, the RGB color values of rice pixel in CA are extracted, and then the RGB color values are converted to HSI color values.

According to the characteristics of HSI color system, this paper uses the hue values H as the color characteristic values detecting the rice process quality. As the rice color extraction area (CA) in this paper is divided into 5 equal sub-regions, the color characteristic values of each sub-region is set to H i (i = 1,2, ... ... 5), then each rice color feature value could be described as follow:

  * 1 2 3 4 5 H H H H H H  , , , , (3)

2.6

The identification method of rice process quality

In this paper, BP neural network model is used to detect the process quality of rice. The rice samples of four forms are respectively selected to be used to image analysis, and to obtain the color feature value for making up of the sample set of the neural network training set. Suppose the detection values of brown rice (BR), first process rice (FR), second process rice (SR), polished rice (PR) are 0、1、2、3, and then the goals set of the neural network training set is (0, 1, 2, 3). Other 40 full rice are respectively selected from the rice samples of four forms to obtain the color feature value for making up of the detection set of the neural network.

The BP neural network in this paper has three layers, the number of input layer neurons is 5, that is the number of the color feature values of rice samples; the number of output layer neuron is 4, and the output signal is (0, 1, 2, 3), and which respectively denotes the rice sample of BR, FR, SR, PR; the number of hidden layer neurons is confirmed according to the accuracy of the test results by using MATLAB software and test set. Neural network identification function is the logistic function:

  ) exp( 1 1 x x f    (4)

3

Results and discussion

Obtain the images of rice samples

First of all, 120 rice are respectively selected from the 4 rice samples which have different process grade, then the rice images are obtained by the rice quality detection vision system. The rice images of four forms samples are shown in Figure 5.

(1)

(2) From Figure 6, brown rice(1) has the hard cuticle on the external layer, so brown rice shows a different color from the rice, and the outer layer of the brown is smooth; After milling, brown rice is processed into the initial processing rice samples(2), and as cutting through the rice milling machine, the outer layer of brown rice most of the stratum corneum epidermidis is cut, thus the entire outer layer of brown rice puts up mixture colors. After the second milling, brown rice is processed into secondary processing rice samples(3), then the outer layer of the stratum corneum epidermidis of whole rice is almost cut and shows the color of the rice, and at the same time, the outer layer of the rice will produce more fine particles, the surface of the rice is not smooth; After the second milling process, rice is polished into rice sample(4) by polishing processing, the fine particle layer of rice surface is removed, and the rice shows glossy color .

(3) (4)

Rice image processing

The goal image of rice can be obtained from the rice image through the rice image process, and the object images of rice are shown in Figure 6.

Fig. 7. Schematic diagram of the color feature extraction region

The color feature values of each 120 samples in four form rice are extracted, and the relationship between the image pixels contained in the color feature extraction region of rice image and the image pixels contained in the rice image is shown in Table 1.

Table 1. The relationship of the image pixels contained in the equal portion circles of the rice image between the sub-region is less than 22 pixel; the percentages of total CA were 20.13%, 20.01%, 19.99%, 19.94%, 19.94%, the error is less than 0.19%; rice image contains an average of 44 016 pixels, the pixels points in CA is 25.79% of total pixel points of rice image; the percentages of the pixel points in sub-regions were 5.19%, 5.16%, 5.16%, 5.14%, 5.14% of the average total pixel points of rice images, the error is less than 0.05%. Therefore, the method of the division of the rice color extraction region into 5 equal sub-regions can insure the rice pixel points in every color feature extraction sub-region are equal.

Variation rules of color feature values

The color feature values were extracted from 120 rice samples for four forms of rice respectively, and then transformed the color feature value from the RGB color space into the HSI color space, and the average values of the color characteristics H of four forms of rice samples were shown in Table 2. From the Table 2, there are some variation rules between the color feature values H of 4 rice samples. The color feature values H i (i=1、2、…… 5) of the same rice samples of different process methods don't have significant variation rules, which are the same with the different structure and distribution of the composition in rice and the milling and polishing process in the different regions of the rice surface. But among the different forms of rice samples, the color feature values H i (i=1,2, ... ... 5) increased significantly on the whole. when the rice sample is brown rice, H values are in the range [1.3624, 1.9821]; when brown rice are milled though first process, H values are in the range [2.2124, 3.1121]; after the second milling process, H values are in the range [2.6696, 3.5502], after the rice polished, H values are in the range [3.0815, 3.9702]. Obviously, this is the same with the removal process of the cuticle layer and the aleurone layer on the surface of the rice by milled and polished.

The distribution histogram of the color feature values H i (i = 1,2, ... ... 5) of 4 rice samples is shown in Figure 8. Consolidated Table 2 and Figure 8, rice samples show the appearance of different colors with the rice milling process; and the color feature values of rice samples show some variation rules.

Identification of rice process quality

The train set is established adopting the color feature values H and detection values of 100 rice samples of 4 forms to train the BP neural network, and then the BP neural network is verified through the color feature values H and detection values of 50x4 rice samples of 4 forms, and the detection result of the number of different hidden layer neurons is shown in Table 3. From the table, when the number of hidden layer neuron is 15, the rice samples' overall accurate rate identification is highest, the accurate rate of brown rice is 94.00%, and the accurate rate of first process rice is 96.00%, the accurate rate of second process rice is 88.00%, and the accurate rate of the polished rice is 92.00%, the overall identification accuracy rates is 92.50%.

From above mentioned, detect the rice process quality can achieve a satisfactory result by constructing the 3 layers BP neural network with 5 neurons in the input layer, 15 neurons in the hidden layer, 4 neurons in the output layer, and discriminate function of the logistic-type function, and using the rice color feature values H extracted from the surface of the rice samples.

Conclusions

In this paper, the method of detecting rice process quality was verified by experiments based on the color and BP neural network.

First of all, the rice images was obtained, then definite color feature extraction region, and then the color feature extraction region is divided into five color feature extraction sub-regions of the same area with concentric circles of different radius. The color values H of the color feature extraction sub-regions are regarded as the color feature value of the rice, and finally a BP neural network of three layers is adopted to detect the process quality of the rice. The experiment's results show that accuracy rate of this method of extracting the hue values of the rice image to detect the process quality of the rice is 92.5%.

Fig. 1 .

 1 Fig. 1. The processing flow chart of rice image

Fig. 2 .

 2 Fig. 2. The detection flow chart of rice process quality by image analysis

Fig. 3 .

 3 Fig. 3. The color extraction region diagram of rice image The extraction region of the color values of rice image (CA) was divided into 5 equal sub-regions (Sa, Sub area) by the concentric circles which have a centroid coordinate(X, Y) and ranging radius. The sub-regions have the number for the Sa i (i = 1,2, ... ... 5) from the inside to the outside of the circle. Set the concentric circles in the CA have the radius of r i (i = 1,2, ... ... 5), the sketch map of 5 sub-regions in CA is shown in Figue 4.

Fig. 4 .

 4 Fig. 4. The sketch map of 5 sub-regions in CA The relationship between the radiuses r i of the concentric circles and the radius R of the rice color extraction region (CA) is:

Fig. 5 .

 5 Fig. 5. Rice sample images of different forms. (1)brown rice samples (BR); (2)the first process rice samples (FR); (3)the second process rice sample (SR); (4)polished rice samples (PR)

Fig. 6 .

 6 Fig. 6. The object images of rice samples (1)object image of BR; (2) object image of FR; (3) object image of SR; (4) object image of PR

Fig. 8 .

 8 Fig. 8. The relationship between the color feature values H of 4 rice samples From the figure 8, as for the different forms of rice samples in the same color feature extraction sub-region, the color feature values H change with the milling process and present a growing trend.Consolidated Table2and Figure8, rice samples show the appearance of different colors with the rice milling process; and the color feature values of rice samples show some variation rules.

Table 2 .

 2 The color characteristics H for four forms of rice samples

	rice sample	Sa1	The color feature value of the color featureextraction region Sa2 Sa3 Sa4	Sa5
	BR	1.3624	1.8812	1.8509	1.9821	1.4798
	FR	3.1121	2.5987	2.5019	2.2124	2.5013
	SR	3.5502	2.9942	3.1421	2.9278	2.6696
	PR	3.9702	3.6774	3.0815	3.7286	3.1869

Table 3 .

 3 The color detection results of the rice sample using BP neural network

		rice samples	The number of hidden layer neurons and the detection results Sa1 Sa2 Sa3 Sa4 Sa5
	BR	the number of rice accuracy rates(%)	45 90.00	44 88.00	47 94.00	46 92.00	47 94.00
	FR	the number of rice accuracy rates(%)	45 90.00	48 96.00	48 96.00	46 92.00	43 86.00
	SR	the number of rice accuracy rates(%)	40 80.00	41 82.00	44 88.00	44 88.00	41 82.00
	PR	the number of rice accuracy rates(%)	43 86.00	45 90.00	46 92.00	45 90.00	46 92.00