Zhanyu Liu
email: liuzhanyu@zju.edu.cn

Jia-An Cheng

Wenjiang Huang

Cunjun Li

Xingang Xu

Xiaodong Ding

Jingjing Shi

Bin Zhou

Hyperspectral Discrimination and Response Characteristics of Stressed Rice Leaves Caused by Rice Leaf Folder

Keywords: Hyperspectral remote sensing, Rice crop, Rice leaf folder, Principal components analysis (PCA), Support vector classification (SVC)

Detecting plant health condition plays an important role in controlling disease and insect pest stresses in agricultural crops. In this study, we applied support vector classification machine (SVC) and principal components analysis (PCA) techniques for discriminating and classifying the normal and stressed paddy rice (Oryza sativa L.) leaves caused by rice leaf folder (Cnaphalocrocis medinalis Guen). The hyperspectral reflectance of paddy rice leaves was measured through the full wavelength range from 350 to 2500nm under the laboratory condition. The hyperspectral response characteristic analysis of rice leaves indicated that the stressed leaves presented a higher reflectance in the visible (430~470 nm, 490~610 nm and 610~680 nm) and one shortwave infrared (2080~2350 nm) region, and a lower reflectance in the near infrared (780~890 nm) and the other shortwave infrared (1580~1750 nm) region than the normal leaves. PCA was performed to obtain the principal components (PCs) derived from the raw and first derivative reflectance (FDR) spectra. The nonlinear support vector classification machine (referred to as C-SVC) was employed to differentiate the normal and stressed leaves with the front several PCs as the independent variables of C-SVC model. Classification accuracy was evaluated using overall accuracy (OA) and Kappa coefficient. OA of C-SVC with PCA derived from both the raw and FDR spectra for the testing dataset were 100%, and the corresponding Kappa coefficients were 1. Our results would suggest that it's capable of discriminating the stressed rice leaves from normal ones using hyperspectral remote sensing data under the laboratory condition.

Introduction

Detecting plant health condition plays an important role in controlling disease and insect pest stresses in agricultural crops, which always lead to yield loss and poor quality [START_REF] Everitt | Using airborne video, global positioning system, and geographical information system technologies for detecting and mapping citrus blackfly infestations[END_REF] . Although pest damage is predominantly concentrated in patches around original foci, the common protecting practice is still to spray agrochemicals indiscriminately over the entire fields [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF] . To minimize economic loss and reduce ecological and environmental pressure due to the superfluous input of agrochemicals, it's necessary to accurately know the pest distribution locations and damage degrees caused by disease and insects [START_REF] Pedigo | Closing the gap between IPM theory and practice[END_REF] so that the most economical amount of agrochemicals can be applied to the infestation patches. It's a pity that the most common method of damage quantification evaluation is still visual survey [START_REF] Moshou | Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks[END_REF] . However, this technique is subjective, labor-intensive, and time-consuming, and it is impossible to accurately estimate the damaged areas and severity over a wide range [START_REF] Kobayashi | Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[END_REF] . Recently, remote sensing techniques have been proved to be effective to monitor insect infestation and disease epidemic in agricultural crops and other plants from ground to satellite platforms, especially in the site-specific crop management of precision agriculture [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF] .

The current studies have shown that plant spectral properties at visible and nearinfrared (NIR) regions vary with the development of specific signatures for a specific stress of a given plant species [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF] . The reflectance or digital number of healthy plants is low in the visible region (400~700 nm) due to strong pigment absorption, is high in the NIR region (750~1300 nm) for cellular and subcellular structure, and is variable in the shortwave infrared (SWIR) region (1300~2500 nm) because of the presence of water vapor, proteins, and other biophysical and biochemical elements [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF][START_REF] Pedigo | Closing the gap between IPM theory and practice[END_REF][START_REF] Kobayashi | Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[END_REF] . In addition to raw reflectance, derivative spectral reflectance and various vegetation indices can also be effective in detecting plant stresses [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF][START_REF] Demetriades-Shah | High-resolution derivative spectra in remote sensing[END_REF] .

Furthermore, various statistical methods, including cluster analysis [START_REF] Li | Hyperspectral identification of rice diseases and pests based on PCA and PNN[END_REF] , decision tree [START_REF] Goel | Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn[END_REF] , partial-least square regression [START_REF] Huang | Detection of Sclerotinia rots disease on celery using hyperspectral data and partial least squares regression[END_REF] , and principal component analysis (PCA) [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF] , have been used with various remote sensing data for detecting plant stresses. Among them, PCA is the most widespread method for hyperspectral remote sensing data because it can solve the high dependence and autocorrelation in adjacent wavebands [START_REF] Panda | Distinguishing blueberry bushes from mixed vegetation land use using high resolution satellite imagery and geospatial techniques[END_REF] . Moreover, the application of artificial intelligence methods including artificial neural networks (ANN) and support vector machines (SVM) in plant stress detection has also been reported recently [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF][START_REF] Liu | Monitoring the rice disease and insect stress with remote sensing (C)[END_REF] . The nonlinear support vector classification machine (viz. C-SVC) had been used to detect the rice health condition at the leaf and canopy levels, which obtained above 85% of the classification accuracy [START_REF] Liu | Monitoring the rice disease and insect stress with remote sensing (C)[END_REF] .

Rice (Oryza sativa L.) is the most important staple food in Asia area and there have been a lot of remote sensing-based researches in rice disease and insect pest damage which harm the leaf lamina, leaf sheath, stem, panicles and other organs [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF][START_REF] Liu | Monitoring the rice disease and insect stress with remote sensing (C)[END_REF] . However, little study was found to discriminate the normal and stressed rice leaves caused by rice leaf folder [START_REF] Shi | Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine[END_REF][START_REF] Huang | Spectral characteristics of rice leaves damaged by rice leaf roller[END_REF] . Rice leaf folder (Cnaphalocrocis medinalis Guen) is widely distributed between 48° N and 24° S latitude and 0° E to 172° W longitude in the rice growing areas of Asia, Oceania, Australia and Africa [START_REF] Khan | A bibliography of rice leaf-folders (Lepidoptera: Pyralidae)[END_REF] . The feeding damage caused by rice leaf folder shows up characteristically in pale stripe in the leaf where the chlorophyll-bearing tissues have been eaten away, and only epidermis and cuticle remains. All feeding damage is strictly linear, i.e., occurs in the longitudinal axis of the leaf between the veins.

The purposes of the present study are (1) to analyze the hyperspectral response characteristics of raw and first derivative reflectance (FDR) of normal and stressed paddy rice leaves caused by rice leaf folder, (2) to identify the principal components (PCs) derived from both the raw and FDR spectra, and (3) to examine the capability of SVM in combination with PCA for discriminating normal and stressed rice leaves.

Methodology

Study Sites

The samples of normal and stressed rice leaves caused by rice leaf folder (Cnaphalocrocis medinalis Guen) were collected in three paddy rice fields at the active tillering stage. One field was located near Yundong village (30°12′N, 120°28′E), and the second in Zhangpanqiao Village (30°06′N, 120°15′E), Xiaoshan District, and the last in the Experiment Farmland of Zhejiang University (30°14′N, 120°10′E), China. The sampling date in the former two paddy fields were on July 27 th , 2007 and the sampling number of normal and stressed rice leaves in all was as equal as 19. The sampling date in the latter and last paddy field was on August 14 th , 2007 and the sampling number of normal and stressed rice leaves was as equal as 35. The rice cultivar names in the three sites were unknown, but all of them belonged to hybrid and Japonica rice.

Foliar Hyperspectral Reflectance Measurement

The normal and stressed rice leaves caused by rice leaf folder (Cnaphalocrocis medinalis Guen), which were verified by plant protection expert, were cut from the rice individuals in the paddy fields. The samples were put into an incubator (25℃) and were transported to the laboratory for further measurements. The foliar hyperspectral reflectance of paddy rice was measured with a portable spectrophotometer (Analytical Spectral Devices Inc., Boulder, CO, USA) through the full wavelength range from 350 nm to 2500 nm. The spectral resolution of the instrument varied from 3 nm less than 1000 nm to 10nm more than 1000 nm, while the spectra were interpolated by the spectrometer software in 1 nm intervals.

Each leaf was positioned on a dark background. The fiber optic sensor with 8° instantaneous field of view (IFOV) vertically pointed to the upper leaf surface about 3 cm height. An incidence angle of 45° was maintained at a standard distance (50 cm) . The light source used was one 50 watt halogen lamp. For each paddy rice leaf, a sampling spectrum was made consisting of five readings and four sampling spectra were measured. The average hyperspectra derived from four sampling spectra represented each leaf using ViewSpec Pro (version 5.6.10). All spectral measurements were made relative to a BaSO4 standard reference-panel, which was observed immediately before and after the spectral measurements of rice leaves.

Foliar Pigment Concentration Measurement

Immediately after foliar hyperspectral reflectance measurements have been taken, the leaves were chipped, weighted and then dipped in 20 milliliter solution (acetone: ethanol: distilled water = 4.5:4.5:1) for 24 hours to extract pigment. The optical density of the extraction solution was measured at 440 nm, 645 nm, and 663 nm by spectrophotometer (Shimadzu UV 2550, Tokyo, Japan). Pigment consists of chlorophyll a (ab. Chl a), Chlorophyll b (ab. Chl b), total chlorophyll (ab. ChlT, which is equal to Chl (a+b)) and Carotenoid (ab. Car) in the study. The formula of pigment concentration (mg/g) was referenced the literature of Tang et al. [START_REF] Tang | Relations between red edge characteristics and agronomic parameters of crops[END_REF] . The statistical information of pigment on rice leaves was shown in Table 1.

Data Preprocessing

Firstly, the hyperspectral reflectance was smoothed with a seven step moving average to suppress the instrumental and environmental noise before these data were further analyzed [START_REF] Kobayashi | Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners[END_REF] . Next, the raw hyperspectral reflectance less than 400 nm and more than 2400 nm were deleted due to severe instrument and system noise. The effective spectral region of FDR ranged from 450 nm to 850 nm. Furthermore, the total dataset was consciously divided into two parts, namely training and testing datasets. The training dataset included the normal and stressed rice leaves caused by rice leaf folder, viz. those leaves which were collected on August 14 th , 2007. The testing dataset was consisted of those leaves which were collected on July 27 th , 2007.

Derivative spectroscopy is proposed to tackle analogous problems such as background signals and to solve overlapping problems in spectral features. In practice, the simplest method of finding derivatives is by dividing the difference between successive spectral reflectance by the wavelength interval separating them [START_REF] Demetriades-Shah | High-resolution derivative spectra in remote sensing[END_REF] . The FDR spectra can provide the slope information on the rate of change reflectance with respect to wavelength [START_REF] Holden | Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy[END_REF] . FDR were calculated to analyze the hyperspectral response characteristics of stressed rice leaves caused by rice leaf folder in this study.

Analytical Techniques

Principal component analysis (PCA) is capable of preserving the total variance and minimizing the mean square approximate errors, and it is also used to identify the dominant modes of the original dataset such that the first principal component (PC 1 ) accounts for the maximum possible proportion of the variance information of the original dataset, and subsequent principal components (PC 2 , PC 3 …) account for the maximum proportion of the unexplained residual variance, and so forth [START_REF] Holden | Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy[END_REF] . The application of PCA has been reported in many studies as an effective data compression technique especially for hyperspectral remote sensing data because it can solve the high dependence and autocorrelation in adjacent wavebands [START_REF] Panda | Distinguishing blueberry bushes from mixed vegetation land use using high resolution satellite imagery and geospatial techniques[END_REF] . PCs were calculated to represent the hyperspectral features of rice leaves in this study.

Support vector classification (SVC), which is a popular technique for data classification, is one of the most important parts of support vector machine (SVM).

The aim of SVC is to devise a computationally efficient way of learning 'good' separating hyperplanes in a high dimensional feature space based on the generalization and optimization theory [START_REF] Cristianini | An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods[END_REF] . SVC was conducted in LIBSVM (Library for Support Vector Machines) developed by Chang and Lin [START_REF] Chang | LIBSVM: a library for support vector machines[END_REF] in our study. The goal of LIBSVM is to help users to easily use SVM as an attached library for SVM. LIBSVM is consisted of C-SVC, ν-SVC, distribution estimation (one-class SVM), and other models. Thereinto C-SVC was adopted in this study. The key parameters of LIBSVM such as kernel function γ and penalty parameter C and other parameters are provided with default selections available.

There were two assessment parameters to evaluate the accuracy of C-SVC combing with PCA in this study. One is Kappa coefficient which is one measure of the level of agreement. A Kappa coefficient of 1 denotes a full agreement and 0 denotes full disagreement between the actual and predictive classification [20]. The other is overall accuracy (OA) which denotes the agreement probability between the predictive and actual classification for each random sample.

Results

Hyperspectral Response Characteristics of Paddy Rice Leaves

Applying hyperspectral remote sensing to precise crop integrated pest management (IPM) is on the basis of spectral response characteristics of paddy rice leaves to crop insect stress in Fig. 1. Fig. 1 showed the hyperspectral difference was obvious between normal and stressed paddy rice leaves caused by rice leaf folder at the nanometer level because there were evident changes in pigment concentration (Table 1), cell and texture arrangement structure and foliar water content due to the feeding damage caused by rice leaf folder [START_REF] Khan | A bibliography of rice leaf-folders (Lepidoptera: Pyralidae)[END_REF] . Furthermore, six wavelength intervals (Table 2) had been calculated to simulate the high resolution geometric (HRG) and vegetation (VGT) sensors of Systeme Probatoire d'Observation dela Tarre (SPOT) satellite based on arithmetic mean of hyperspectral reflectance for universal application of SPOT's image in vegetation monitoring all over the world. The simulated six wavelength intervals clearly indicated the hyperspectral difference between normal and stressed paddy rice leaves from visible spectrum region (PA: 460~690 nm, B0: 430~470 nm, B1: 490~610 nm and B2: 610~680 nm) to NIR (B3: 780~890 nm) and SWIR (1580~1750 nm).

As shown in Fig. 1 (a), the raw hyperspectral feature of the normal paddy rice leaves was different from the stressed leaves through the full spectra range (400~2400 nm). Because paddy rice foliar pigment concentration decreased obviously (Table 1), hyperspectral reflectance of stressed leaves increased 2.9, 1.9 and 3.3 times than the normal ones in the visible blue (B0), green and near red (B1) and red (B2) light region (Table 2), respectively. However, the hyperspectral reflectance of stressed leaves decreased 20.7% and 9.7% than the normal ones in NIR (B3) and one SWIR (2.08~2.35μm) region, respectively. But in the other SWIR region, the hyperspectral reflectance increased 28.9% in comparison with the normal ones.

The amplitude of FDR for stressed paddy rice leaves was smaller than that of normal ones as shown in Fig. 1 (b). Maybe it's because the stressed paddy rice leaves looked like 'white' color after rice leaf folder fed; the hyperspectral reflectance of the stressed paddy rice leaves had lower change rage with respect to wavelengths. Both of the peak wavebands of normal and stressed leaves in the blue light region were located at 521 nm. Same phenomenon occurred in the green light region, the peak waveband (569 nm) of stressed leaves was equal to normal leaves. The red edge position of normal leaves was at 702 nm, but the stressed leaves shifted toward short wavebands about 5 nm.

Principal Component Analysis

The first principal component (PC1) explained 72.1% of the variance of the raw hyperspectral reflectance in rice leaves for the training dataset. Fig. 2 showed both eigenvector of PC1 and the spectral reflectance difference between the stressed and normal leaves. PC1 had a loading of 0.958 to the reflectance difference of raw spectra between the stressed and normal rice leaves. The results of the principal component analysis (PCA) revealed that the cumulative contribution of the front three components of the raw hyperspectral reflectance and FDR for the training dataset were 98.1% and 66.2%, respectively (Table 3). Generally speaking, when the cumulative contribution of the front several components is above 80~85%, the remaining components always are omitted in further analysis [START_REF] Holden | Spectral discrimination of healthy and non-healthy corals based on cluster analysis, principal component analysis, and derivative spectroscopy[END_REF] . So the front eighteen components could explain 85.6% of the variance of FDR for training dataset, which were regarded as the principal component spectra (PCs) in this study.

Results of Support Vector Classification Machine

The nonlinear support vector classification machine (referred to as C-SVC) was used to automatically detect the health condition of paddy rice leaves. The C-SVC model was built to differentiate the stressed rice leaves from normal ones by using the training dataset consisting of dependent (i.e. normal and stressed rice leaf categories) and independent (i.e. PCs) variables for calibration purpose. The C-SVC model was run repeatedly with multifarious sets of values of γ (kernel function) and C (penalty parameter) at least three times for cross-validation in order that the optimum classification result could be obtained. When γ was RBF (radial basis function) and C was 1, the C-SVC model was able to discriminate paddy rice leaves categories without any error. Then the C-SVC model was validated by the testing dataset and the classification results were given in Table 4. Not only for the raw hyperspectral reflectance, but also FDR spectra, none of normal or stressed paddy rice leaf was classified mistakenly. The stressed rice leaves had obviously spectral difference from normal ones after the rice leaf folder fed (Fig. 1 and Table 2), which resulted in the perfect classification result by C-SVC model integrated with PCA.

Discussions

In this study, C-SVC was employed to detect the health condition of paddy rice leaves by integrating it with PCA using raw hyperspectral reflectance and FDR. The results indicated that the stressed rice leaves caused by rice leaf folder presented a higher reflectance in the visible (430~470 nm, 490~610 nm, 610~680 nm) and one shortwave infrared (2080~2350 nm) region, and a lower reflectance in the NIR (780~890 nm) and the other SWIR (1580~1750 nm) region than the normal leaves (Fig. 1a and Table 2). These results were consistent with our previous research [START_REF] Shi | Hyperspectral recognition of rice damaged by rice leaf roller based on support vector machine[END_REF] , and were similar to other's results for geenbug-damaged wheat at the leaf or canopy level [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF] . However, our results were not in agreement with the report from Huang et al. [START_REF] Huang | Spectral characteristics of rice leaves damaged by rice leaf roller[END_REF] who concluded that the spectral reflectance of rice leaf decreased in the green (530~570 nm) region with the increase of the damage degrees. Perhaps because in our study the rice leaf folder almost fed on the whole upper surface leaf, there were little pigment concentration in the remaining downer surface leaf than the stressed leaves caused by greenbug [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF] or rice leaf folder [START_REF] Huang | Spectral characteristics of rice leaves damaged by rice leaf roller[END_REF] in others reports. FDR spectra were also analyzed from a different view. Perhaps because the stressed paddy rice leaves looked like 'white' color after rice leaf folder fed, the amplitude of FDR for stressed leaves was smaller than that of normal ones (Fig. 1 b). Not only the stressed paddy rice leaves, but also the normal ones, the peak wavebands of blue and green spectrum region were located at 521 nm and 569 nm, respectively. But the stressed leaves shifted toward short wavebands about 5 nm, which suggested that the red light spectrum region was more sensitive to plant pest damage than the blue and green regions. Similar results occurred in other results [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF] .

The front several PCs were found to be sensitive to the reflectance and digital number of healthy and nonhealthy plants as well as their linear combinations according to current research [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF] . Same results were also found in this study. PC1 could explain 95.8% of variance in the raw reflectance difference between the stressed and normal rice leaves (Fig. 2). The front three PCs would reveal above 85% of cumulative contribution except the FDR spectra of training dataset (Table 3). Subsequently, the classification was almost perfect with the combination of C-SVC classifier and PCA for discriminating the stressed rice leaves from normal ones with the obtaining PCs (Table 4). Maybe it's because the stressed rice leaves had obviously spectral difference from normal ones after the rice leaf folder fed (Fig. 1 and Table 2).

Conclusions

Even though the present study would suggest that it's capable of discriminating the stressed rice leaves from normal ones using hyperspectral remote sensing data under the laboratory condition, this technique is probably still not very practical for detecting the health condition of agricultural crops under the field conditions [START_REF] Yang | Differentiating stress induced by greenbug and Russian wheat aphids in wheat using remote sensing[END_REF] . More external factors, including atmospheric (e.g., illumination, cloudy shadow), edaphic (e.g., soil type, soil moisture), and biotic (e.g., crop variety, leaf area index) conditions as well as field management strategies, coexist at the canopy level [START_REF] Liu | Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[END_REF] , which consequentially bring about too much noise in hyperspectral reflectance data and lower classification accuracy for stressed crops and their organs. Therefore, more studies are needed to explore the spectral response characteristics of rice crops under fungi and insect stresses of different levels in outdoor conditions.

The samples of rice leaves only had two levels, namely, normal and stressed rice leaves caused y rice leaf folder. However, the rate of rolling leaf is employed to determine the damage severity caused by rice leaf folder in practice. Future study should include more stress levels in order to discriminate not only the normal rice leaves from the stressed ones but different stress status.

Fig. 1 .

 1 Fig. 1. Average hyperspectral reflectance of rice leaves collected on August 14 th , 2007.

Fig. 2 .

 2 Fig. 2. Loading plot of the first principal component (PC1) derived from the raw hyperspectral reflectance. S-N meant the hyperspectral reflectance difference between the stressed and normal paddy rice leaves.

Table 1 .

 1 Statistical description of paddy rice foliar pigment concentration (Unit: mg/g)*.

	Statistical parameters	Normal rice leaves, N=35	Stressed rice leaves, N=35
		Chl a	Chl b	Car	Chl a	Chl b	Car
	Min.	1.63	0.57	0.60	0.88	0.36	0.35
	Mean	2.06	0.71	0.76	1.35	0.52	0.54
	Max.	2.53	0.83	0.96	1.90	0.73	0.79
	Std. dev	0.19	0.06	0.08	0.25	0.10	0.10
	VC	9.2%	8.0%	10.1%	18.7%	18.7%	19.4%
	* The paddy rice leaves were collected on August 14 th , 2007.

Table 2 .

 2 Average canopy hyperspectral reflectance of rice crops at six wavelength intervals representing the High Resolution Geometric (HRG) and Vegetation (VGT) of SPOT (Unit: %)*.

	Spectra range	July 27 th , 2007 Normal(n=19) Stressed(n=19) Normal(N=35) Stressed(n=35) August 14 th , 2007
	Pan: 490~690 nm	10.1c	22.6a	9.6c	20.9b
	B0: 430~470 nm	5.2c	15.0a	5.5c	13.4b
	B1: 490~610 nm	12.3c	22.8a	11.6c	21.4b
	B2: 610~680 nm	6.8c	22.3a	6.7c	20.1b
	B3: 780~890 nm	57.9a	45.9c	54.7b	41.1d
	SWIR: 1580~1750 nm	39.2a	35.4b	35.7b	33.2c
	* Reflectance values within a row followed by the same letter are not significantly different
	from each other by Duncan test at α = 0.05.

Table 3 .

 3 Percentage of explained variance for the front three PCs (Unit: %)*.

	Spectral	Training dataset (August 14 th , 2007)	Testing dataset (July 27 th , 2007)
	types	PC1	PC2	PC3	CC	PC1	PC2	PC3	C
	Raw	72.1	24.6	1.4	98.1	70.4	26.8	1.6	98.8
	FDR	47.9	14.1	4.2	66.2	68.4	11.1	7.6	87.2
	* CC denotes the cumulative contribution.

Table 4 . Error matrix for C-SVC from the testing dataset with PCs derived from raw and FDR spectra.

 4

				Predictive classification
	Actual		Raw spectra			FDR spectra
	classification	(OA=100%, Kappa coefficient=1)	(OA=100%, Kappa coefficient=1)
		Normal	Stressed	Total	Normal	Stressed	Total
	Normal	19	0	19	19	0	19
	Stressed	0	19	19	0	19	19
	Total	19	19	38	19	19	38

Acknowledgements This research was supported by the China Postdoctoral Science Special Foundation Project (201003712), the Start Research and Fund Profect of Hangzhou Normal University (2011QDL23), the National Basic Research Program (973) of China (2010CB126200), the Agro-Industry R&D Special Fund of China (200903051), and the National Hi-Tech Research and Development Program (863) of China (2007AA10Z205).


Lulu Ge

Daoliang Li
email: li.daoliang@163.com

Liu Yang
email: yangliu@cau.edu.cn

Wenzhu Yang
email: wenzhuyang@163.com

Image Segmentation of Pseudo-Foreign Fibers in Cotton on the basis of Improved Genetic Algorithm

Keywords: Cotton, Pseudo-foreign fibers, Genetic algorithm, Image segmentation, Threshold

In the foreign fibers cleaning process, pseudo-foreign fibers are often mistaken for foreign fibers, this result not only seriously affects the detecting precision of foreign fibers cleaning machine, but also doubles the time of cleaning up lint. As for false identification problem of pseudo-foreign fibers in cotton, this paper proposes a new approach for fast segmentation of pseudo-foreign fibers in cotton on the basis of improved genetic algorithm. This improved genetic algorithm reduced the searching range for calculating optimal threshold from 0~255 to 100~220. The calculating speed in this stage was improved more than twice in average. The fitness amendments formula is also proposed to improve genetic algorithm disadvantage, at the same time, this solved issues of "premature", and converging to global optimal solution difficultly in the traditional algorithm. The results show that the algorithm has high speed, accuracy, anti-interference and so on.

Introduction

Sorting out foreign fibers in cotton is an urgent solved problem [START_REF] Ronghua | Classification and identification of foreign fibers in cotton on the basis of support vector machine[END_REF]. Foreign fibers have affected the quality of cotton products seriously [START_REF] Wenzhu | Fast recognition of foreign fibers in cotton lint using machine vision[END_REF]. Automated visual inspection (AVI) system is a main tool at present for real time foreign fibers detection in lint. The theory of this system is color differences of image processing technology [START_REF] Daoliang | Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine[END_REF][START_REF] Jinming | Study on a foreign fiber detecting system with linear CCD[END_REF]. The concept of color difference is based on that impurities of lint cotton and the cotton background have different colors. Hence, the impurities which has similar colors and shapes with foreign fibers were thought as foreign fibers to be removed, which is called as "pseudo-foreign fibers in cotton", also referred to be "pseudo-foreign fibers".

Pseudo-foreign fibers in cotton have various kinds, such as cottonseed, grass blade, boll shell, cotton leaf, cotton stick, weed, stringy cotton, stringy cotton, colored cotton and oiled cotton, etc. [START_REF] Baolin | Difficulties of machine vision technology in the foreign fiber removed from the cotton[END_REF]. These pseudo-foreign fibers are very similar to the foreign fibers in color, size and shape. However, compared with foreign fibers, pseudo-foreign fibers harm hardly to the cotton into yarn, dyeing, bleaching and other aspects. On the new standard GB1103-2007 of cotton, these pseudo-foreign fibers in cotton are ruled to follow the normal process of impurities to remove, and then determine level according to standards cotton classification [START_REF]General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China[END_REF].

Meanwhile, the traditional methods of image segmentation, such as Otsu method, and the watershed method, are tested to process the pseudo-foreign fibers' image. The histogram information generated by these methods has only one peak, and the peak threshold is more concentrated, and segmentation effect is not satisfactory [START_REF] Wenzhu | Toward Image Segmentation of Foreign Fibers in Lint [J][END_REF].

Due to false identification by cleaning machine of foreign fibers, pseudo-foreign fibers affected seriously the detection accuracy of the foreign fibers cleaning machine, also affected the efficiency of the foreign fibers cleaning machine. The time of cleaning up lint cotton is thousand times to increase, and also dragging processing progress of lint cotton.

At present, there is not pseudo-foreign fibers' testing equipment in the world.

Meanwhile, the pseudo-foreign fibers in cotton make cotton production and processing more difficultly. Hence, China Agricultural University and China Cotton Machinery & Equipment Co., Ltd. are jointly developing detection equipment on line of foreign fibers. Thereinto, Image segmentation is the key of improving the equipment measurement accuracy and reducing false recognition rate, so a suitable image segmentation algorithm of pseudo-foreign fibers determines the latter target recognition and classification of good or bad.

In order to overcome the shortcomings of the traditional methods and segment pseudo-foreign fibers target quickly, this paper puts forward to a new image segmentation method on the basis of improved genetic algorithm.

2

Samples and Methods

Sample Preparation

Due to the wide of pseudo-foreign fibers is very large, totally 150 kinds, so the typical pseudo-foreign fibers were chosen to do this research which included grass blade, cottonseed, stringy cotton, boll shell, cotton leaf, cotton stick, weed, stringy cotton, ginned dead cotton, waste paper, colored cotton and oiled cotton. Adequate pure cotton lint with no pseudo-foreign fibers was also prepared for making the lint layer. All these pseudo-foreign fibers' samples were provided by China Cotton Machinery & Equipment Co., Ltd.

Image Acquisition

Colorful images are captured by the color line scan camera of DALSA under high-brightness LED lighting. Shooting frame rate is 30fps @ 4000×500 color images.

Image processing used the Pentium-M processor (CPU), clocked at 2.0GHz, 2GB of memory for the IPC, and software development tools for Visual C++ 6.0.

The steps of image acquisition are as follows. An opening machine is performed to make pseudo-foreign fibers away from the cotton layer as far as possible which is 80 cm wide and 2 mm thick, and then the images are acquired by the DALSA line scan camera. The capture card saves the images following the order of cotton layer passing by.

Method Selection

There are many image segmentation methods. According to the different treatment strategies, segmentation algorithms can be divided into serial and parallel algorithm.

The serial algorithm determines the current point as edge depending on edge detection to get the result of the previous point, but the parallel algorithm only depends on the current point and its neighborhood points. Therefore, the edge detection operator can act on each pixel of the image in parallel algorithm, but the serial algorithm depends on start point and the previous point. That means parallel algorithm's judgments and decisions can be made independently and simultaneously. In the serial algorithm, the results of early treatment can be used by subsequent processing. Therefore, serial segmentation algorithm needs more time to process data and process control is more complicated.

Speed and accuracy of an algorithm are key factors for the online visual inspection system. Hence, parallel algorithm is chosen to process pseudo-foreign fibers' images more suitably. Meanwhile, due to pseudo-foreign fibers have many kinds and colors, and pixels spread are extensive, so all of these specifications are fit for highly adaptability and flexibility of parallel algorithm. Here genetic algorithm is the most appropriate one. Using this algorithm to process threshold, it ensures segmentation's accuracy, and faster speed of this algorithm is more attractive.

Traditional Genetic Algorithm

Genetic algorithm is a bionic algorithm, the living beings in nature have undergo elimination and evolution continuously from the initial single-celled organisms to multicellular organism, and eventually human beings who have high intelligence and high adaptability of primates [START_REF] Xinquan | Two-dimensional thresholding and genetic algorithms in image segmentations[END_REF][START_REF] Lei | Two-dimensional entropy method based on genetic algorithm[END_REF]. The process of evolution totally proved Darwin's "natural selection, survival of the fittest" theory [START_REF] Zhengfeng | License plate image segmentation based on modified genetic algorithms[END_REF][START_REF] Yanghui | Several populations competed genetic algorithm and its property analysis[END_REF][START_REF] Lingyan | An image segmentation method using the entropy of histogram and genetic algorithms[END_REF]. It told us the living beings who adapt to environment will have chance to breed, and others who can not accustom environment will be eliminated. Though iteration of dozens of generations, the quality of biological communities will improve [START_REF] Hongyan | Image Thresholding Segmentation Based on 2D Maximum Entropy Principle and Improved Genetic Algorithm[END_REF]. Genetic algorithm used this principle to encode the parameter space, also do the steps of selection, crossover and mutation and other operations, then use the fitness function value the to evaluate images, the image information which is evaluated up to standard will be preserved into the next generation, the unqualified image information will be eliminated [START_REF] Zhenjie | A Method of Marrow Cell Image Segmentation Based on GA[END_REF].

In addition, the genetic algorithm has many advantages for image segmentation of pseudo-foreign fibers. It is a probabilistic, iterative global search algorithm for groupment [START_REF] Yongjun | Chromosome Biomedicine Image Processing Based on Genetic Algorithm[END_REF], the search space does not need restrictive assumptions as other image segmentation algorithms, such as: continuous, derivative exists, histogram single peak characteristics, etc. [START_REF] Yangting | Image Segmentation Approach Based on Improved Genetic Algorithm [J][END_REF]; the searching is not blind, it gains on the best value step by step to finish the "evolution".

Improved Genetic Algorithm

As the traditional genetic algorithm has appeared "throwback" phenomenon in the process of choosing individual [START_REF] Goldberg | Genetic algorithms in search, optimization and Machine Learning[END_REF], in other words, offspring's fitness function value is less than the parent generation; meanwhile, in order to improve the processing speed of genetic algorithm, this paper did several improvements.

Improvement of Searching Range for Calculating Optimal Threshold

The genetic algorithm is a parallel global optimization algorithm, although the processing speed has obvious advantages than other algorithm, the selection of groupment is random, so it will waste some time. After tested a lot of samples, the distribution of the best segmentation threshold of the pseudo-foreign fibers images is concentrated from 120 to 200. In order to avoid the limitations of the tests and not rule out some special pseudo-foreign fibers which have special color, finally, the optimal threshold distribution is conformed from 100 to 220. This algorithm has reduced the range of searching, greatly decreasing the optimal threshold search time, the average processing time is tens of milliseconds, and the calculating speed in this stage was improved more than twice in average.

Combination of Elitist Strategy and Roulette Wheel Method

As the genetic algorithm is a random selection of individuals, it is possible that the individual whose fitness value is higher has not been selected, but the individual whose fitness value is lower is selected. This is "throwback" phenomenon. According to the principle of bionics analogy, a conclusion can be acquired that the individual whose fitness value is lower may produce the next generation whose fitness is not high, that means these individuals have poor quality. To overcome the drawbacks of these individuals, elitist strategy is led in to choose some individuals who have high fitness value to come into the next generation directly, and other individuals use roulette wheel method to select according to the share of individual fan-shaped area of the roulette selection probability.

2.5.3

Fitness Amendments Formula

In the initial generations of the genetic algorithm may produce some individuals whose fitness value are high, finally these individuals may eventually fill the entire group, which would make the crossover which is used to generate new individuals lose its meaning, even more importantly, the convergence of the algorithm may converge the part optimal value ahead of time, rather than the global optimal value, this is called "population premature" problem. Hence, Goldberg linear stretch formula is fixed to improve the fitness function, which reduces the differences of fitness value between individuals with higher fitness with other individuals; it can maintain population diversity effectively, balance maximum and average fitness value as the optimal fitness function. The amendments formulas are as follows:

) ( ) ( ) ( ) ( 5 . 1 ) ( ) ( ) ( ) ( ) (
T s B A         1 ) 1 ( ln ) ( ) ( ) ( . ( 3
)      1 0 ln n i i i T p p H . ( 4
)     s i i i S p p H 0 ln . (5)    s i i p P 0 . ( 6
)
Where

Image Segmentation Steps on the basis of Improved Genetic Algorithm

The image segmentation steps on the basis of improved genetic algorithm are as follows:

(1) The establishment of the fitness function According to formula (2) -( 6).

(2) The amendment of the fitness function According to formula (1).

(3) The establishment of populations and coding Several individuals are selected randomly, then they are encoded binary code for the gray value of the individual, each binary code likes a chromosome, and these individuals' chromosomes are as the first generation. After calculating the fitness function value for each corresponding individual, the sum of group fitness value can be calculated, and then the fitness value of every individual also can be obtained, finally following the elite strategy and roulette wheel method, the individuals can be extracted and copied.

(4) Crossover

Which is followed the new crossover probability proposed in the above.

(5) Mutation

The process modeled the process of organism's gene mutation: a gene on chromosomal locus may mutate as its allele, it usually caused certain phenotype changes. Here this refers to each individual's binary code from 0 to 1 on certain part, or from 1 to 0. For converging to the maximum fitness value as quickly as possible, the mutation probability is set as follows in this paper: the first thirty generations is 0.02; from the thirty to fifty generation is 0.03; the latter fifty generations is 0.02.

Then the new individuals are chosen to compose a new generation instead of the original individuals.

(6) Termination conditions When the algorithm reaches the maximum generations, or after 10 generations the group's highest fitness value has not changed, the algorithm stops.

(7) Image segmentation The group's highest fitness value is defined as the optimal threshold to perform image segmentation.

Results and Analysis

The paper processed twelve kinds of pseudo-foreign fibers' image, and compared segmentation effect and the processing time with the Otsu method and the traditional genetic algorithm. Partial results are shown in Fig. 123and Table 1-2. It can be seen from the results:

(1) This segmentation algorithm can split the pseudo-foreign fibers precisely, and retain a good edge of pseudo-foreign fibers, the segmentation images are clear, which are shown in Fig. 3. As the pseudo-foreign fibers' images distributed unevenly, target has a wide range of gray scale, by using the Otsu method calculate the optimal threshold, the between-class variance becomes smaller, the probability of faulting the target into the background increases, take Fig. 1(a) as an example, the bottom of the grass blade was mistaken into the white background, that means the pseudo-foreign fibers target are retained half-baked, nearly the same segmentation effect, and can split basically the pseudo-foreign fibers target, but the overall effect is not as good as the algorithm of this paper, and the effect is shown in Fig. 2.

(2) It can remove a lot of noise, including Gaussian noise produced by the optical components and impulse noise of environment. Three kinds of segmentation methods have similar anti-interference capabilities.

(3) Processing time of improved genetic algorithm is only tens of milliseconds, high processing speed. However, the traditional genetic algorithm and Otsu method need relatively long time, which are shown in Table 1.

(4) The optimal segmentation threshold distribution of improved genetic algorithm is between 100 to 220. Specific data is shown in Table 2. Table 2 shows that the optimal segmentation thresholds which were the following three segmentation methods calculated are within a reasonable range, that is the optimal segmentation threshold should not exceed the target ceiling's gray-scale 220; the optimal segmentation threshold by using Otsu method obtained is too large or too small, a larger fluctuation range, sometimes closes to the limit, the target image information has been lost, the optimal segmentation threshold by using the genetic algorithm has low threshold range, the target image has also been retained completely.

(a)

Conclusion

By using digital imaging technology, this paper has processed the images of pseudo-foreign fibers in cotton, gray-scale characteristics of pseudo-foreign fibers and background are chosen as a starting point, presented a new image segmenting method on the basis of improved genetic algorithm. After the experiment contrast test, the method is verified that it can segment image precisely and clearly, also remove a lot of noise. Meanwhile, it avoided the target segmenting incompletely for a long time and other shortcomings of traditional methods. This new method reduced the searching range for calculating optimal threshold from 0~255 to 100~220. The calculating speed in this stage is improved more than twice in average. This paper proposed the fitness amendments formula, and also solved "premature", converging to global optimal solution difficultly issues of the traditional algorithm; this paper combined elitist strategy and roulette method to improve the algorithm. The results

show that the algorithm has high-speed, accuracy, anti-interference and so on.

Fig. 1 (

 1 Fig. 1(b), 1(c) also exist such situation. Traditional genetic algorithm has very

Fig. 1 .

 1 Fig.1. Segmentation results of the original gray images using the Otsu's method.

Fig. 2 .Fig. 3 .

 23 Fig.2. Segmentation results of the original gray images using the traditional genetic algorithm.

  Crossover is the main method of genetic algorithm to generate new individuals, it is similar to the homologous chromosome synapsis in biological evolution, in other words, a certain bit which comes from an individual's binary code exchanges with another individual corresponding bit to produce a new individual. Crossover probability value is very important. the value is the larger the better normally, the range between 0.4 to 1. Different targets, different values. If the value is too large,

	S which can make	H	(s	)	reach the maximum threshold is defined as the optimal
	threshold for image segmentation,	H	(s	)	is also the best fitness value.
	2.5.4	Determination of the Optimal Crossover Probability
		S is the segmentation threshold,	) H is the image histogram entropy (s
	which is also the fitness function,	H	(s	)	,	H	(s	)	is the total entropy for the target
									A	B
	A and background B,	H ,	H is the entropy of all pixels and s the following
					T		S
	thresholds (including s), P,	p is the probability of gray level for the following
							i
	threshold values of s (including s) and the probability of every gray level . Threshold

perfect pattern of group activities will be destroyed, the evolution of computing will be interrupted; if the value is too small, the speed of generating a new individual is too slow. Therefore, this paper improved Crossover probability value as follows: in the first 30 generations, the crossover probability is designed as 0.6, each generation has six individuals to executing crossover, other individuals only copied themselves directly into the next generation; in the post 70 generations, the crossover probability is designed as 0.8, because the latter generations' fitness value is relative concentration, this crossover probability can increase group diversity, each generation has eight individuals to executing crossover, and the remaining two individuals simply copied themselves directly into next generation.

Table 1 .

 1 Image segmentation processing time comparison of pseudo-foreign fibers /ms.

				stringy
		grass blade cottonseed	cotton
	Otsu method	2942	406	437
	traditional genetic algorithm	31	47	265
	improved genetic algorithm	15	43	79

Table 2 .

 2 The optimal segmentation threshold comparison of pseudo-foreign fibers in cotton.

		the optimal segmentation threshold S
	image		traditional genetic	improved genetic
		Otsu method	algorithm	algorithm
	grass blade	159	175	184
	cottonseed	155	169	174

Acknowledgements

The authors thank National Natural Science Foundation of China ( 30971693) and Ministry of Education of People's Republic of China (NCET-09-0731), for their financial support, and China Cotton Machinery & Equipment Co., Ltd. for providing foreign fiber samples and technical support.


Wenshen Jia

Ligang Pan

Jihua Wang

Wenfu Wu

Yang Li

Yuange Qian

Research and Design of an Agricultural Scientific Instruments Classification and Code Management System

Keywords: instruments, agriculture, information systems, Browser/Server model

As China enlarged the investment in agriculture, the agricultural scientific instruments promoted rapidly. It required us to face the questions scientifically such as the procurement, management, resources sharing, evaluation and etc. in agricultural scientific instruments. Because of the lack of unified classification and code standards, it greatly limited the scientific procurement, efficient management, resources sharing evaluation in agricultural scientific instruments. Therefore, the existing classification and code standards cannot meet the demand also. This paper studies and develops the agricultural scientific instruments classification and the code management system based on B/S (Browser/Server) structure and ASP.NET technology. The database used the Microsoft SQL Server, the Server application used C#, the browser based on XHTML + JavaScript. This system on the basis of database, realized the inquiring mode in the fuzzy sense of four kinds of different ways to query data in agricultural scientific instruments. And it also supports the Chinese character automatically converts spell brief-code etc. This system has a significant effect on agricultural science instrument information management and sharing .It helps to improve the assets utilization rate and can regulate the state-owned assets management system also.

Introduction

The agricultural scientific instruments development has four notable features:Firstly, the amount is large. The total industry demand and the total agricultural scientific instruments quantity are huge. It had been increased on agricultural scientific instruments investment in recent five years. The constructive agricultural financial investment funds had invested 60% of the agricultural scientific instruments [START_REF] Fang | Preliminary Study in Sharing Scientific Instrument and Equipments in China[END_REF] . Secondly, the range is complete. Modern agriculture is a complex system, covering a complete range of equipment which involved in complex and diverse. It is related to heat, electricity, light, chromatography and other large instrument [START_REF] Jiang | Modern agriculture in Chinese the retrospect and prospect[END_REF] . Thirdly, the agricultural scientific instruments developed rapidly. According to statistics, the instruments imported about 20 billion totally from 2008 to 2009, of which, the agricultural scientific instruments occupied a large proportion. The agricultural scientific instruments presented a quick development [START_REF] Zhuang | Development of Science and Technology in Instrumentation, Measurement and Control Area of China[END_REF] . Fourthly, the feature is distinctive. The agricultural scientific instruments fully embody agricultural characteristics, the instruments and equipments covered a multitude of special features. For example, canola speed measuring device, pesticide residues speed measuring device [START_REF] Hui | High intelligence and pesticide residues speed measuring devices to come out[END_REF] 、yellow aspergillus toxin speed measuring device, etc..In Chinese agriculture application, the speed measuring device provides the necessary technical support by preferential agricultural policies. It is a prominent characteristic. Due to the four characteristics of agricultural scientific instrument, it put forward the new age for purchase and management. This paper will combine the agricultural industry business with modern high-tech information services to realize the agricultural scientific instruments name and classification code fast converting speed, apparatus, the ownership of the classification of quick inquiry. Greatly improved the agricultural scientific researchers work efficiently.

Research status

Because of lacking the unified classification and code standards in agricultural scientific instruments, it greatly limited the agricultural scientific instruments procurement, scientific efficient management, sharing and evaluation. The nation invested vastly in agriculture in recent years. The rapidly investment mainly reflects in the level of the instruments. Since everyone lacks of common standard in agricultural scientific instruments classification and code, and a similar instrument used in the different studies, may name differently. The direct result is each individual experts should have a very high professional judgments in the project review process. Also it brought a series of problems. For example, if the government investment grasp accurate? Is it efficient? Whether embodied the instruments value in the research front-line? It lacks of a unified evaluate judgment. According to the purchasing items of Chinese Academy of Agricultural Sciences from 2006 to 2010, the annual funding for the purchase instruments had more than 150 million RMB, and it had gradually increasing year by year. It also had a lot of difficulties in sorting out, managing and evaluating all of these instruments. A lot of instruments cannot manage in information, sharing the large instruments become impossibly. The lack of consistent classification and coding standards, became the bottleneck in agricultural scientific instruments for purchase, management and sharing [START_REF] Fengqin | Strengthen the Research Funds Management of Agriculture[END_REF] .

The current classification and code standards cannot meet the agricultural scientific instruments procurement, management and evaluation of demand. In recent years, the national science and technology drafted 'National classification and coding large-scale scientific instruments table'. In the investigation of the application, it for 500000 RMB of above large-scale scientific instruments are classified and coded. In agricultural field application, applying the classification standard, more than 80% of the agricultural scientific instruments are classified in the quality inspection instrument. It can not reflect the characteristics of agriculture. Agricultural research can not reflect the level completely. It should have no value reflect to come out. The classification standard cannot meet the agricultural field. In 1992, the environmental industry drafted environmental protection instrument classification and named standards, but the standard is simple relatively, which of instrument coverage is small. Therefore the standard cannot adapt to the information management needs.

3 System design and architecture

Agricultural scientific instruments classification method

The system uses the line classification, classification of agricultural scientific instruments, according to instruments type, characteristics and standard. This system is divided into categories of agricultural instruments, categories, sub-category, groups and type of four levels. In the hierarchy, it is divided into the categories of upper class, which divided into the categories of lower class. Directly into a category by the next level out of all kinds of projects, it known as parity class. As a kind of division, it is the same as a benchmark for parallel relationship between the classes. Lower class and upper class categories are subordinate relationship. As a kind of relationship, it sort between not cross, cannot repeat, and only the upper class. Classification in turn, should free layer or additional layers.

The first level instruments of 'category' with the first, second digital code, the second level instruments of 'sub-category' with the third, fourth digital code, the third level instruments of 'group' with the fifth, sixth digital code, the fourth level instruments of 'type' with seventh, eighth digital code.

Agricultural scientific instruments coding method

According to the current and to be used in quantity of agricultural scientific instruments, it determined by each level two digital code, a total of eight digital level the code structure. Its classification structure shown Fig. 1.

The system of agricultural scientific instruments is used in line classification, at a higher level on each class (group), it only contains all of the following lower-level classes (groups). The code base is each level differences between the characteristics of the object. It is incompatible features on each level. To lower level code is actually higher levels the code and low levels of the code composite code. The third and fourth layer is no longer sub-classification, in its code up '0' until the eighth. To meet the agricultural scientific instruments enlargement ductility and coding extensibility principle, each layer classification of all equipped for the item, asylum has not been listed s to code 99.

For agricultural machinery classification method directly used for in 'NY/T 1640-2008 Agricultural machinery classification [START_REF]The agriculture department's agricultural machinery test appraisal station、The agriculture department's agricultural machinery maintenance research institute[END_REF] ' standard of classification method and the classification number. In this system, the second layer of sub-categories of agricultural machinery with the classification code in the same category code. The third layer group classification of agricultural machinery in the last two class code is consistent. The fourth layer type code and classification of agricultural machinery in the last two items is the same code. Tropical crop of mechanical classification method directly used criteria of 'NY/T 1560-2007 Tropical crops mechanical classification [7] ' classification method . Code encoding method according to the coding system.

XX XX XX XX

The fourth layer (type)

The third layer (group)

The second (sub-category)

The first layer (category) It uses VB.NET, c # or Jscript.NET programming language to realize the middle three-layer structure (business logic layer) design, namely using the ASP.NET presentation layer to complete the design [START_REF] Fabio | NET Data services C# Senior programming[END_REF] .

The .NET development platform application created in Common Language Runtime environment bottom CLR control operation. It is used to load the application, make sure they cannot wrong to perform for the corresponding safety license application, verification, and implementation in operation, finally the completion of the wash them away. Class library provides that applications can be written XML data, on the Internet communications, such as access to database code [START_REF] Rahmel | Use Visual Studio 6 development Web database applications[END_REF] .

In considering the system requirements, the user acceptance and research foundation, after this study adopts the current application extensive Windows Server 2003 +.NET + Microsoft SQL Server 2005 system. As for data management and information service network system, and its structure using Browser/Server mode is the best choice. Not only convenient for users to use, but also saving a lot of development and operation of investment. For different needs, it based on the underlying database query detailed information model. In the development process, highlighting the technical characteristics of humanity, it is from the perspective of user-friendly features and interface design.

System architecture. Agricultural scientific instruments classification and code management system structure using current applied widely and technology mature Browser/Server mode. If there is Internet access and a browser installed on the computer, this mode can be any user data access and query, which it is the biggest characteristic. Comparing client /server mode to Web page instead of the client software, it is the so-called 'thin client'. Which does not require client software maintenance and upgrades, there is no cross-platform issues. Thereby reducing the burden of agricultural research workers, reducing the cost of data maintenance, but also it makes any changes, effective immediately, to ensure information timeliness, accuracy and completeness [START_REF] Qi | C/S and B/S architecture research[END_REF] . Provide data query, the server program run by the router and the network firewall connected to the Internet, through the access Internet users of computer, login inquires the interface to access and data query. In the function, the information system have three layers of logical structure, including the presentation layer, function and data layer.

As a result of a three-layer structure, it is making the deployment, maintenance and upgrades easier and flexible. As shown in Fig. 2. is the main function modules of the system. This system contains query, demonstration and maintenance three function modules, which the query module mainly focus on the serial number query, the spelling query, the category query and the name query. Maintenance module contains the users to access classification management, instrument code entry, instrument code ownership by category and sub-category, etc. 5 Key algorithm

Agricultural scientific instruments names fuzzy query

Normally it used the SQL statement inquires the text fields, in both sides of the query add '%' wildcard, reoccupy 'LIKE' operator perform a search. However table 1 agricultural scientific instruments classification naming is very complex, and usually the inquires the strategy may produce user input contents leak check or the name of the error types. Therefore this study had developed an agricultural scientific instruments name fuzzed algorithm. The algorithm of the operation process: 1) Pretreatment:

It remove the input string without actually meaning characters, such as space, parentheses, '-' and '.' ;

Conclusions

The agricultural scientific instruments classification and code management system will solve the problem of lacking of unified classification and code standards in agricultural scientific instruments. For the convenience of the user's query information and sharing instruments, this study researched and developed the knowledge on the fuzzy query and the Chinese characters. It improved the efficiency of agricultural scientific instruments inquiries, the agricultural scientific instruments' name fuzzy query and Chinese characters automatically converts spelling brief-code algorithm, it also reduced the rate of false query check and the chance of leakage. It is the function of the realization of a certain innovation. The system regulates the flow of agricultural scientific instruments and procedures to share. Thereby it reduced the ambiguity due to the non-standard users information, and it can save the research costs, reduce the unnecessary wear and tear. The current agricultural scientific instruments will be further developed and improved in integrated information systems and the intelligent direction. It will distribute a bar code for each scientific instruments in future, and continue to develop the low-cost hand-held mobile terminals. The bar code scanning equipment can check the information and classification as soon as possible. Thus to reach the aim of sharing the equipment effectively.

Fig. 2 . 3 . 3

 233 Fig. 2. Coding structure schematic drawing

Fig. 2 .Development operation environment 4 . 1 4 . 2

 24142 Fig. 2. System Diagram

Table 1

 1 2) Digital processing: It analyze string is involved in Chinese or Arabic Numbers, if have, to the Chinese and Arabic Numbers exchange; 3) Fuzzy treatment: It mask '%' insert into the query string and the end of the characters, thus finish and between the whole fuzzed process. Compared with the usual inquires the strategy, this algorithm is characterized by be inserted into the wildcard between each character, in order to adapt to all kinds of complicated instrument name. Combined with pretreatment and digital processing, this algorithm can successfully lookup table 1 listed in the various types, which can effectively improve inquires the efficiency, reducing leakage and the chance of error. Agricultural scientific instruments classification code

	Classification code	Agricultural scientific instruments
	70070205	Meteorological satellite issued equipment ground station
	70070206	Meteorological satellite receiving equipment ground station
	70070207	Meteorological satellite data collection equipment ground station
	5.2

Chinese characters automatically converts spell brief-code algorithm

  Agricultural scientific instruments classification and code management system database saved thousands of instruments information. The code name translation is one of the important functions. In common Chinese text query string is the input of Chinese characters, but this method requires too many keystrokes. For example, inquires the instrument for 'Meteorological satellite issued equipment ground station' in Chinese, need the Chinese input method input 'qixiangweixingdimianfabuzhanshebei', keystroke at least 34 times, the efficiency is low. The Chinese method of brief-code inquiry can effectively improve the speed of information retrieval equipment. Brief-code is the first Chinese spell brief-code. For example, the same 'Meteorological satellite issued equipment ground station' inquiring record, brief-code inquiring the only need to enter 'qxwxdmfbzsb'. Example shows that the use brief-code inquires to target records quickly, reducing keystrokes, greatly improving the efficiency of query. Then the system uses fuzzy inquiry method. Actually using, you input 'qxw' three letters can quickly lock inquiring the instrument of the relevant information.

	The following are Chinese characters automatically converts spell brief-code algorithm.
	// Create two different encoding object
	Encoding unicode = Encoding.Unicode;
	// Create GBK code object
	Encoding gbk = Encoding.GetEncoding(936);

// Unicode string will be converted to bytes

Acknowledgment

This work is supported by National Science and Technology Support Project 'farmland restoration and land management research and demonstration of key technologies', subject 'disaster destroyed farmland and urban wastewater irrigation farmland restoration technology integration and demonstration'. [2011BAD04004]


Shujing Wan

Chengming Zhang

Jiping Liu

Yong Wang

Hui Tian

Yong Liang

Jing Chen

The Self-adaptive Adjustment Method of Clustering Center in Multi-spectral Remote Sensing Image Classification of Land Use

Keywords:

As one kind of remote sensing images of land use composed by various categories of surface objects difficult to obtain multi-distribution model of class spectral feature, analyzing the spectral characteristics of LU of multispectral RS imagery, this paper presents a self-adaptive adjustment of clustering center method.

Depending on the intercepted situation of the cluster centers between different features to conduct split, the sub-centers obtained are as the sub-category features and the cluster centers assemble to characterize category model which is better to deal with the problems of LU category composed by various surface objects and category model not satisfying multivariate normal distribution. As there are much differences between the many centers features in the unit of category area, so the selection of training area and the determinants of rules are easy. The results of experiment indicate that the LU classification accuracy is increased between 4% and 6% with this method.

Introduction

The LU is classified by the influence degree and the purpose of human activities [START_REF] Cingolania | Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units[END_REF] which refers to the activities of the human imposed on the surface, and the land use categories belong to information categories. The object classification of multi-spectral remote sensing images is based on the spectral characteristics of ground objects, and the objects categories belong to spectral classes. Using multispectral RS imagery for LU classification, a convenient multivariate statistical model is in general not available for the multispectral characteristics of LU class [START_REF] Lin | Region multi-center method for land use classification of multispectral RS imagery[END_REF][START_REF] Chen | Study on Combined Classifier Based on Error Analysis[END_REF][START_REF] Dong | The Calculation Method of Inverse Camera Response Function of a Single Image based on Kernel Estimation[END_REF] .

Looking from the existing literature, there are mainly three solutions to solve this problem: firstly, try best to make the samples satisfy the multivariate statistical distribution, such as with the unsupervised clustering method, to choose the dominant and continuous pixels as training samples, then classify with statistical pattern recognition method [4]. Though this solution reduces the differences of samples, the training samples don't contain all ground objects' information. So the difference between classificatory pattern and the ground object is rather great to result in classification error [START_REF] Chen | Rough set theory based object-oriented classification of high resolution remotely sensed imagery[END_REF][START_REF] Xiaoping | Classification of Remote Sensing Images based on Ant Colony Optimization[END_REF] .

Secondly, make use of elevations, textures, boundaries and all other secondary information to reduce the dependence on the spectral information, whereas the secondary information add the dimension of (*):corresponding author the classification feature vector, and the vector multivariate statistical model is more difficult available So we commonly adopt multi-classifier confusion method to classify different types of information, to reduce the classification vector dimensions of single classifier and improving the clustering performance of the classification vector, then fuse the results of several classifiers [START_REF] Hur | A Hybrid Classification Method Using Error Pattern Modeling[END_REF][START_REF] Lucas | Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping[END_REF][START_REF] Mao | Remote sensing image classification based on formal concept analysis[END_REF] . However, the problem of spectral information statistical distribution is not taken account in these methods, the classification accuracy increases is limited, and even it may lower to some classes [START_REF] Bian | A novel spatial co-location pattern mining algorithm based on k-Nearest feature relationship[END_REF] .

Thirdly, take the un-statistical pattern recognition method to solve multivariate statistical distribution, such as decision tree method. This method makes use of the class subset to form the boundary of branch decision tree [START_REF] Cheng | Remote sensing image classification method supported by spatial adjacency[END_REF][START_REF] Hanhui | An improved K -means cluster algorithm[END_REF] , but it is difficult to divide subset and to form rules for the method.

Analyzing the multi-spectral feature of LU in RS image, this paper presents a new method, Region Multi-Center (RMC), for LU classification of multi-spectral RS image. In this method, the classification cell is region, classificatory pattern is formed by clustering centers of training samples, and adopt rule-based method to classify.

Multi-spectral remote sensing image analyses 2.1 distribution of Within-class distance

The theory basis of land use remote sensing image classification is: in the same condition, the same objects should be able to show some internal similarities of theirs, and performing in remote sensing image, the pixels formed by same objects should have the same or similar spectrum information features and space information features. The pixels characteristic vectors which represent the same objects of will distribute in the same feature space area. In fact, although with existing of same objects in different spectral the same kind objects may have greater diversity in the spectrum, that a small class always presents similar spectral features.

Therefore, it is necessary to study the characteristic vector and category of the distribution center distance vector in order to construct a new classifier. In order to give facilities to analysis, confirmed a distance interval analysis unit, first as a threshold to distance of the sample clustering and calculating the clustering center to category center distance, obviously they are consistent, and more meet statistical conditions.

,m j2 , …,m jl },l=1,2, …,k, m jl =1/n l   ni i 1 xi(jl) ,j=1,2,…,c; l=1,2,…,k,the distance of within-class is d jl =||m jl -m j ||,
mapping with the ordinate of n l and n j 's percentage and the abscissa of d jl , called the within-class distance distributions.

The Analysis of the Land use classification of multi-spectral

The experiments use the area of Shandong province Laiwu city Xueye reservoir with the ETM + images in spatial resolution of 30m, a total of eight bands, the size of 1500 x 1200 pixel and the time is on May 31, 2007.

The experiments choose samples to analyze the infra-class distance distribution of multi-spectrum in four kinds of land use category as water, residential areas, forest land and bare soil. The rule of selecting samples is that samples should cover as many kinds of pixels in ground objects as possible.

The samples should be more and the distribution range of samples should be wide to comply with the above principle. According to the rule, each class respectively chooses the sample 16384 pixels. Fig. 2 shows the rough region of the choosing samples. In essence, the " same objects in different spectral " phenomenon is due to in a variety of reasons the value deviation of similar features in some bands or all bands is too large resulting that feature vectors of the class in the feature space distribute excessively decentralization and it is difficult to attribute to the same cluster as we expect. Although it can reach the goals mandatorily by changing the cluster center vectors and the effective radius vectors that it may result in that classify the pixels which not belong to the class to assign to which may reduce the classification accuracy. To better illustrate this problem, we take a classification of the two-band image and for example, assume that the image will be divided into A, B two classes that the distribution of each pixel is as Figure 4.

Fig.4. The schematic of the wrong classifications result

In Figure 4, as the pixels of the class B are distributed in the two regions, the resulting cluster centers and the distance between the two regions is too large, resulting in identification range (ie, blue circle) is too large. As a more extreme case, but another class A area was surrounded in the identification of the B range, surrounded by an orange rectangle should be divided into Class A of the pixel, is divided into Class B wrong. To solve this problem, cluster centers can be used to split ways. After the end of the study sample, the intersection of the cluster center to determine if the two cluster centers according to the radius of the resulting range of the identification part of the intersection, then the cluster centers need to be split.

Adaptive step

Adaptive cluster center is split by continued implementation of cluster centers,concrete steps to split is :

(1)During the study of sample, the same category in the normal way to generate cluster centers, cluster centers in the sample vector and the maximum distance as the radius of the cluster centers identified.

(2)Calculate the result cluster centers two by two to determine whether the two cluster centers intersects , in another words, two cluster centers are identified areas of overlap. If they intersected, treat as (3).

(3)Split a larger range between the two cluster centers. Class from the sample to be split to choose one sample, randomly select a distance less than the original value of the largest identified as the threshold, the sample is divided into different subsets, separately for each study, resulting in the respective cluster centers and not split the class with the intersection of the judge, if there is still a subset of the cluster centers with which they have intersected, then re-elect a smaller threshold, re-split.

After the split, the original sample a large set of complete small sample to be broken down into multiple sets, as a sub-category treatment. In this way, each -Spectrum with different material‖of the surface features can be divided into several sub-categories, each sub-class corresponds to a pattern. The same sub-classes of spectral characteristics are similar.

Experiment

Experimental data is shown in figure 2 Looking at the results from the classification point of view, parallelepiped classification method is often a part of the misclassification phenomenon, mainly due to the similarity measure threshold T impact. Through investigation and interpretation, the method divides a number of unclassified woodland into forest land and bare soil incorrectly. Using minimum distance classification method, the number of pixels to be classified significantly increased, to be classified and sub-phenomenon of mixed woodland is more serious, there is water affected by the shadow of the phenomenon of misclassification. K neighbor method can be a good way to distinguish watershed land and naked body while could not recognize forest, some forest land is divided into body of water mistakenly.

Maximum likelihood method, while classifies water bodies better would make serious mistake in the classification of the forest and bare soil. K-nearest neighbor method can be a good way to distinguish watershed land and naked body while could not recognize forest, some forest land is divided into body of water mistakenly. BP neural network method has better classification of various types of effects, but there is still some vegetation types not distinguished.

Adaptive process of cluster centers and the results of classification

Using the cluster center adaptive classification method, training samples through the formation of water bodies, residential areas, woodland, bare soil, respectively, the optimal number of model centers 50, 20, 50 and 10, the experimental results shown in Figure 6.

The classification accuracy of different models and types of training samples that shown in Table 2. Comparing different methods of classification results, we can see, the use of this method of classification, the results of the spots decreased; Table 7 Comparison Table 6 Classification accuracy increased by 4 percent.

Conclusions

This paper presents a RMC method for LU classification of multispectral RS imagery. The classification cell is region, and the classificatory pattern is represented by infra-class center set of the training samples. The method fully considers the characteristics that a kind of LU class is covered with several ground objects, and the classificatory pattern does not meet multivariate statistical distribution.

To RMC method, the multispectral feature is more dispersal, the classification accuracy is improved greatly. There are two classification rules of RMC method, one is the type amount of classificatory infra-class centers which the pixel of the region cell belonged to, the other is the percentage of the pixels belonged to the class from the whole region cell pixels. The rules express sufficiently the basic idea of LU classification system and classification based on the area ratio of ground objects to whole region. Meanwhile, it fuses the region information and the spectral information. Owing to the large difference of RMC feature, it is simple to establish the classification rules and to select training samples.

RMC method which multi-centers confirmed by clustering solve preferably the problems of the division of subset and the confirmation of boundary of decision tree method. Furthermore, the method can be applied to single class recognition and can adopt different classification rules for different classes. RMC method can increase the LU classification accuracy of multispectral RS imagery between 4% and 6%.

1 njFig. 1 . 1 nl

 111 Fig.1.Category and within-class center schematic

Fig. 2 .

 2 Fig.2. The remote image of Xueye Reservoir in experiment

Fig. 3 . 3 . Adaptive method of cluster centers 3 . 1

 3331 Fig.3. Graph of special distance distributing of

Fig. 5 .

 5 Fig.5.The results of classification

Fig. 6 .

 6 Fig. 6. The extraction results

Table 1 .

 1 Accuracy of common classification method (%)

	Classification method	water forest	town	Arable land	bare field	Average
	Maximum likelihood	85.6	84.1	76.3	82.9	86.5	83.03
	Minimum distance method	87.7	86.8	72.4	85.6	85.3	83.03
	K Neighbor method	90.2	85.7	74.6	84.2	85.1	83.9
	Neural network	87.0	85.2	81.7	84.1	85.9	84.78

Table 2 .

 2 Classification accuracy of different models and types of training samples

	water	forest	town	Arable land	unclassified	Average
	88.5	87.8	86.8	89.3	86.2	87.72
	88.2	87.6	87.0	88.9	85.3	87.40
	87.9	88.3	86.4	89.2	86.0	87.56
	86.4	88.5	88.7	89.0	84.1	87.34

Acknowledgements

This study has been Funded by (1)Basic Research Fund of Chinese Academy of Surveying and Mapping under Grant (Contract Number: 7771109), (2)Innovation Plan for College Students of Taian(Contract Number:2010D2014)