Zhenxiang Huang
email: zhenxianghuang@126.com

Jietao Xie

Juan Wu

Bo Peng

Research on Computer Vision-Based Object Detection and Classification

Keywords: Computer Vision, Detection, Classification

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The interest of this topic in agriculture is motivated by the promise of many applications, such as agricultural and food product inspection [START_REF] Brosnan | Improving Quality Inspection of Food Products by Computer Vision--a Review[END_REF]. Compared with traditional mechanical technologies, computer vision methods enable more efficient detecting and classifying. For example, categorizing fruit for uniformity can benefit from the advances. Based on computer vision methods can rapid grading fruit quality with respect to color [START_REF] Lee | Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping[END_REF], shape, crack and defect, and also have sufficient adaptability to variations, while traditional methods cannot.

Impacted by nature or other complicated factors, agriculture products are distinct with each other greatly. Some issues are often unavoidable, for example, lighting conditions vary over time, image blurs or tilts, or object is partly occluded. These are the reason why the topic is challenging, interesting and worth researching. In this paper, a detailed overview of current main advances in the field is provided. Object detection and classification are discussed independently. Moreover, we focus on color and shape these two salient features for detection.

Detection

The literature related to this topic is vast, and we attempt to present two main corresponding groups in the following sections: color-based and shape-based approaches. Shape and color features are two key indicators for fruit and vegetables when they are to be inspected or classified. If agriculture products were improperly fertilized or sprayed with insecticide, deformation or abnormal color will be induced.

Color-Based Approaches

Color information is the most significant visual feature in agriculture products and it has good performance on invariance of size and view point change. Color based approaches usually aim at segmenting out the desired colors of interest region for further processing. The main limitation with these methods is that they are very vulnerable to nature illumination change.

Color Thresholding. Color thresholding is particularly useful and simple for segmenting image with strong contrast between object and background. By setting the color value range and classifying pixels, thresholding can be done and object can be segmented. When a pixel matching, if its color value is among the reference range, the pixel can be called a required object pixel, if not it will be considered as a background pixel. RGB (Red-Green-Blue) is the most intuitionistic color space. Its advantage is fast enough for real-time performance, due to no need for color space transformation.

HIS or Other Color Space Transformation. HIS (Hue-Intensity-Saturation) color space has good consistency with human vision. Significantly eliminating the influence of brightness changes is its most attractive characteristic, by dividing an overall intensity value from saturation and hue two values. But it will take some time and hardware cost to transform the captured image represented by RGB value into HIS value. CIE model takes human visual characteristics and environmental conditions into consideration, and gets more immune to lighting. However, setting parameters is complicated when applied to specific environment. There are other more color spaces like HSV and L*a*b* [START_REF] Mendoza | CalibSrated Color Measurements of Agricultural Foods Using Image Analysis[END_REF].

Color Indexing. Color indexing is a fast, straightforward and effective detecting method. Its fundamental principle is comparing the colored objects of two images with their color histograms, as color histogram is used to index the object and store into the template database. The potential problem is its computation will grow enormously in complex outdoor scenes.

2.2

Shape-Based Approaches

We divide shaped-based approaches into two categories: boundary-based approaches and region-based approaches [START_REF] Moreda | Shape Determination of Horticultural Produce Using Two-Dimensional Computer Vision -a Review[END_REF]. The former encodes the object's boundary, including Chain code, Fourier descriptor [START_REF] Guo | Spatio-temporal Saliency Detection Using Phase Spectrum of Quaternion Fourier Transform[END_REF], Hough transform [START_REF] Maji | Object Detection using a Max-Margin Hough Transform[END_REF], spline estimation and bending energy. Many statistic features can be used to describe simple shape region, such as area, projection, Euler number, centrifugal rate, rectangular degrees and direction. As for complicate region, we could divide the observation into cells, each of which encodes part of the observation locally.

Object shape detection techniques are usually used for further segmentation. Most of them require big computation and attention about position, viewpoint and partial occlusion.

Fourier Descriptor. Fourier Descriptor is the Fourier transform coefficient of the shape boundary curve, which is a very classical method in transform domain. Its main advantages are based on mature theory of Fourier analysis, and invariant to position, rotation and size under certain circumstances. Also, high accuracy can be acquired with only a few low order coefficients. However, it is very sensitive to noise, and influenced by the curve shape and initial point's position.

Hough Transform. Hough transform is originally used for detecting lines and circles, but gradually its related transforms have been generalized to detect arbitrary shapes. The essence of these approaches is the transformation from image space to the defined parameter space. When the results accumulated in parameter space, it can be used to detect or describe the straight lines or curves in the image. This method is inherently robust, as noise, gap, partial deformation and occlusion can be handled [START_REF] Eduardo | Implementation of Hough Transform for Fruit Image Segmentation[END_REF], but appear a decreased strength. The potential issue is extensive computation and memory hungry.

Moment. Assuming grayscale image normalized as a probability density of bivariate random variable, the random variable can be described by moment. Moment is a kind of linear characteristic. It can be used to describe local features of binary or gray images. This approach in a manner is invariant to image translation, rotation and scale. Usually, moments include invariant moment, Legendre moment Zernike moment and others. Their shortcoming is difficult to present the object's local information and hard for high order moment to directly associate with object's visual features.

Classification

Relying on the same features extracted from the object, good classification approaches can get better results. Three commonly used methods exist are described below.

Template Matching

This method is mainly for searching maximum matching [START_REF] Ren | General Traffic Sign Recognition by Feature Matching[END_REF]. All required object features to be classified are stored in a database, including considerable variation in performance, viewpoint, and illumination. Each potential object is normalized in size and compared with every template of the same feature. Template matching is fast and easily to modify for new classes. Its potential issue is time-consuming when geometry transform is complex. The issue can be compensated with genetic algorithm due to its powerful searching ability, quick response and less templates for matching.

Neural Network

This method is currently one of most successful and widely used learning classification algorithms [START_REF] Torres | Multinomial Logistic Regression and Product Unit Neural Network Models: Application of a New Hybrid Methodology for Solving a Classification Problem in The Livestock Sector[END_REF]. Its advantage is that it is tolerant to fault and is powerful to classify. Through training the neural networks with specific color, shape pictogram, features of interest region can be recognition. Its commonly models contain Back Propagation Network, Multilayer Perception Network, Hopfield Network, Radial Basis Network, etc.

Support Vector Machine

Support Vector Machine extended statistic learning theory to classification, employing Structural Risk Minimization principal to improve generalization capability. It is able to solve problems with respect to nonlinear [START_REF] Li | Bayesian Face Recognition Using Support Vector Machine and Face Clustering[END_REF], high dimension, local minimum and also invariable with viewpoint.

Discussion

Various commonly detecting and classifying techniques based on computer vision have been presented. A comparison of performances of these techniques is given in Table 1.

Color and shape are two essential features for detecting. Color-based approaches are fast and straightforward. The shortcoming is vulnerable with illumination changes, not suitable for weak or reflective conditions. Compared to color-based approaches, shape-based methods face more limitations. When detecting object's boundary, they should be immune to changes, shape imperfect, or partial occlusion. But the issue can be compensated with color-based approaches.

Neural network is very popular for classification. Its performance depends on which what network architecture is applied and how well the network is trained. Support Vector Machine is invariant to rotation, size, slightly tilted and even partial occlusion. Template matching is fast and easily to extend with new classes, but computationally costly when geometry transform is complex. However, if it combined with genetic algorithm, its response rate can be improved. Moreover, the hybrid method wouldn't require abundant templates to be prepared carefully. Some of these approaches are robust but computation hungry, while others are simple but unable to tackle changes. Research in this field is significant and useful, deserving wider attention.

Conclusion

A

Table 1 .

 1 research of the popular object detection and classification based on computer vision has been present in this paper. The research includes description of: (1) existing object detection methods associated with color or shape, (2) important methods developed to tackle the object classification problem, and (3) comparison of performances of these techniques. Comparison of detection and classification approaches.

	Name	Advantage	Limitation
	Color Thresholding	Simple, fast.	Sensitive to illumination.
	HIS color Transform	Immune to lighting changes.	The transform will cost
			computation.
	Color Index	Straightforward, quick.	Great computation time in
			complex scenes.
	Fourier Descriptor	Invariant to viewpoint.	Accuracy degrades if
			curve shape or initial point
			is not desired.
	Hough Transform	Allows slightly shape imperfect or	Computationally	costly
		occlusion.	and memory hungry.
	Moment	Invariant to viewpoint.	Hard to present local
			information.
	Template Matching	Quick and easily to extent for new	Imperfect shape may pose
		classes.	a problem.
	Neural Network	Dynamic handle changes, fast	Demand large training
		matching.	data for good network.
	Support Vector	Invariant to viewpoint, allow partial	Difficult	for	multi-
	Machine	occlusion.	category classification and
			massive samples.


Pingping Li
email: lipingping@nwsuaf.edu.cn

Yongjie Cui
email: cuiyongjie@nwsuaf.edu.cn

Yufeng Tian
email: tianyufeng@nwsuaf.edu.cn

Fanian Zhang
email: zhangfanian@nwsuaf.edu.cn

Xiaxia Wang
email: wangxiaxia@nwsuaf.edu.cn

Shuai Su
email: sushuai@nwsuaf.edu.cn

 Automatic Detection of Kiwifruit Defects Based on Near-Infrared Light Source

Keywords: Image processing, Near-infrared light intensity, Automatic threshold, Manual threshold, Model threshold, Linear relationship Near-infrared light source 7. Kiwifruit

A mathematical model that expresses the relationship between Near-infrared light intensity and automatic threshold for automatic kiwifruit surface defect detection was established. By applying different levels of Near-infrared light intensity to machine vision system, 268 images were collected. Then the images were processed with MATLAB using the method to detect kiwifruit defects based on Near-infrared light source .The obtained 268 sets of data on Automatic Threshold T 0 and Manual Threshold T 1 were divided into 19 groups according to different aperture and light intensity. After processing data, a series of linear equations about the relationship between Near-infrared light intensity and Automatic Threshold T 0 , with function fitting coefficient of R 2 > 95% was obtained. Finally, relationship between T 0 and T 1 was analyzed according to the effectiveness of image processing results and constant P was introduced to revise Automatic Threshold T 0 . Thus, a mathematical model needed to gain kiwifruit defects detection threshold, namely Model Threshold T, was established.

Introduction

The kiwifruit, or often shortened to kiwi in many parts of the world, is the edible berry of a cultivar group of the woody vine Actinidia deliciosa and hybrids between this and other species in the genus Actinidia. It's nutritious and has high medicinal value [START_REF] Rashidi | Classification of Fruit Shape in Kiwifruit Applying the Analysis of Outer Dimensions[END_REF]. China is one of the major areas in producing kiwi. Currently, kiwifruit postharvest sorting processing is still performed manually and its surface defects judgment depends on human completely. Standard Sphere Method [START_REF] Tao | An Adaptive Image Transform for High-speed Fruit Defect Detection[END_REF][START_REF] Feng | Study on Identifying Measurement about Default of Fruit in Computer Vision[END_REF][START_REF] Fu | Gray Level Transform Model of Ball Image and Its Application in Citrus Image Correction[END_REF][START_REF] Ying | Color Transformation Model of Fruit Image in Process of Non-destructive Quality Inspection Based on Machine Vision[END_REF] which uses machine vision technology to separate the fruit surface defects has achieved good results. However, with the traditional RGB [START_REF] Cheng | Inspection of Mildewed Rice Seeds Based on Color Feature[END_REF] [START_REF] Zhu | Defect Segmentation of Tomatoes Using Fuzzy Color Clustering Method[END_REF], CCD imaging systems [START_REF] Pang | Study on External Defects Classification of Navel Orange Based on Machine Vision[END_REF][START_REF] Yang | Prickly Ash Appearance Quality Detection Based on Computer Vision and its Implementation in MATLAB[END_REF][START_REF] Diaz | Comparison of Three Algorithms in the Classification of Table Olives by Means of Computer Vision[END_REF][START_REF] Blasco | Computer Vision Detection of Peel Defects in Citrus by Means of a Region Oriented Segmentation Algorithm[END_REF], the angle between the camera and edge light reflection direction of sphere and ellipsoid fruits remains very large. According to Lambertian light laws of reflection, the fruit edge and surface defects both have lower gray level. Therefore it is difficult to detect and distinguish the defect. In addition, there is a challenge in uniform illumination with the RGB and CCD systems. To overcome these difficulties, near-infrared hyper spectral is used by many research institutes to test external qualities of agriculture products such as maturity of strawberry [START_REF] Nagata | Predicting Maturity Quality Parameters of Strawberries Using Hyperspectral Imaging[END_REF], bruises of strawberry [START_REF] Nagata | NIR Hyperspectral Imaging for Measurement of Internal Quality in Strawberries[END_REF] [START_REF] Jasper | Detection of Bruises in Strawberies by Hyperspectral Imaging[END_REF], apple [START_REF] Zhan | Determination of Chestnuts Grading Based on Machine Vision[END_REF] and chestnut [START_REF] Wen | Dual-camera NIR/MIR Imaging for Stem-end/Calyx Identification in Apple Detect Sorting[END_REF] defects etc.

There are two assumptions in Standard Sphere Method. First assumption is that the shape of fruit is either standard sphere or ellipsoid. Second assumption is that light illumination in machine vision field is uniform. Under these conditions, optical system consisting of lens and camera can be a linear system [START_REF] Li | A Study on Navel Orange Grading System Based on Computer Vision[END_REF]. In the case of kiwifruit, its shape is quasi-ellipsoid. With the Near-infrared light source, the issue of fruit surface reflective area can be overcome as it plays the role of uniform illumination according to preliminary studies [18][19]. This paper aims at collecting images of defective kiwifruit with different light intensity by adjusting the Near-infrared light intensity, then processing images by detection methods for kiwifruit surface defects grading, and finally analyzing the relationship between binarization threshold of kiwifruit defects and Near-infrared light intensity, to establish a mathematical model.

2

Material and Method

Test Materials and Equipment

Test kiwifruit samples were "Qin Mei" cultivars. They were bought from Xizhai Village, Qinghua Country, Mei County, Shaanxi Province. All of the fruits (total 182 samples) were picked up on site and kept in cartons. Weight of a single fruit ranged from 69.6g to 224.0g. Tests were carried out at College of Mechanical and Electronic Engineering, Northwest A&F University. Firstly the fruits were classified; according to the type of defects such as sunburn, parasitic spot. Then intact fruits were scratched to make them defective.

In terms of equipment, lamp house was made of black organic plastic material to avoid external light interference. The camera used was DALSA CCD camera (matching dedicated image acquisition and debugging software Sapera Cam Expert) and the lens was Camera FUJINON HF16HA-1B (aperture range 1.4~16.0). PC used to save data after image processing was Lenovo ThinkPad E420. LFX2-100IR850 Near-infrared light source and dedicated power supply PD-3024-K1320-014 with Light source extension line (1m, 24V) also consisted the test equipment. As a background a white soleplate was provided to enhance the contrast of the background and kiwifruit. The distance between camera FUJINON HF16HA-1B and kiwifruit was 370mm and distance between Near-infrared light source and kiwifruit was 110mm, as shown in Fig. 1.

LFX2-100IR850 Near-infrared light source needed to be attached to the light source dedicated power supply CCS PD-3024-K1320-014 with a light source extension line (1m, 24V).There are 16 brightness coarse adjustment gears and 16 fine adjustment gears respectively. Fig. 1 shows pictures of Near-infrared plane illuminator LFX2-100IR850 and light source dedicated power supply CCS PD-3024-K1320-014. The light source needed to be warmed up for 30 minutes before usage.

Research Program

As shown in Fig. 2, the left dotted line box shows the image processing of kiwifruit defects detection [START_REF] Li | A Method for Surface Defects Detection in Kiwi Fruit Classification[END_REF]. Although this method is able to extract the fruit surface defects, there are two problems exist: Problem 2: As shown in Fig. 2, the automatic defection threshold T 0 will produce error and it will influence the accuracy of fruit defection.

To extract the fruit defects threshold automatically, this paper proposed a method based on Near-infrared light intensity with Automatic Threshold T 0 and thus established a mathematical model. The dotted line box on the right is the solution diagram. As T 1 obtained by manual analysis will produce error. Therefore, Automatic Threshold T 0 was introduced to solve the problem, where T 0 is automatically generated by the image processing program and it reduces the systematic errors effectively. T 0 cannot be used to detect defect directly. After comparing T 1 with T 0 , a correction constant P was introduced to revise T, as T= T 0 +P.

Test Methods and Procedures

Scratch wound is one of kiwifruit surface defects which is the most difficult defect to be extracted. So we took kiwifruit with scratch wound as objects to establish the relationship between Near-infrared light intensity and scratch defects and to determine the automatic threshold of surface defects.

Test procedures:

Tab. (3) Process the numbered images using the method described in [START_REF] Li | A Method for Surface Defects Detection in Kiwi Fruit Classification[END_REF], and record T 0 , T 1 and image processing results.Tab.2 is part of the test data records.

(4) Change the Near-infrared light intensity from 103 to 111, gain T 0 using the method as mentioned in Step (3), and adjust the threshold interval to gain T 1 .

It is obtained by adding a constant P to T 0 , as shown in Tab.4.

(5) Finally, analyze and process the data with Microsoft Excel. Although the final threshold used in image processing is defect detection threshold, the mathematic model expresses the relationship between T 0 and Near-infrared intensity. The reason is that T 1 is a manual selected threshold according to the effectiveness of image processing results. If T 1 is selected to analyze data, it will increase the error. Thus T 0 obtained by MATLAB is used to establish the exact relationship between threshold and light intensity. Then analyze the relationship between T 1 and T 0. Finally introduce a correction constant P to revise T 0 .

Tab.

Mathematical Model

The data were analyzed using Microsoft Excel, and the data in Tab.2 were used to produce the function graph in Fig. 3. The intensity of Near-infrared was from 103 to 111, and the automatic threshold interval was from 0.0900 to 0.4000. Fig. 3-a is the linear diagram of T 0 and light intensity, in Tab.3, we established the equation T 0 =0.0339x-3.3884, R 2 =0.9992. Fig. 3-b is the linear diagram of T 1 and the light intensity and we can find out the equation T 1 =0.0614x-6.1688, R 2 =0.9844. The function fitting degree in Fig. 3-a is higher than that in Fig. 3-b. This is because the T 0 is free from the human error of T 1 .

Similarly, we processed the 19 groups' data and the results were recorded inTab.3. As a result, the function relation graphs between Near-infrared intensity and T 0 was gained. It was linear. All of the 268 groups of data were processed by this method, the results were shown in Tab.3, with R 2 > 95%. Thus the relation between Near-infrared intensity and T 0 can be defined as follows:

B Ax T   0 . (1)
Where: T 0 , x, A, B stand for automatic threshold, Near-infrared intensity, light intensity coefficient, Near-infrared fine adjustment influence coefficient, respectively.

(1) In Tab.3, the functions from 01 to 11 were gained under the same aperture and light intensity with both brightness coarse and variable fine adjustment. They showed linear relationship. In addition, value A and B are variables.

Value A increased as the light intensity enhanced, while value B had the opposite trend.

(1) The functions from 12 to 19 were gained under the same aperture and light intensity with brightness fine adjustment and variable coarse adjustment. They also showed linear relationship. In addition, value A didn't change as the light intensity enhanced, while value B increased with light intensity enhancing.

(2) Considering that T 0 was not used to detect the fruit surface defects directly, it was necessary to define the correction constant P value. The relationship between Near-infrared intensity and defection threshold was defined as follows:

P T T   0 .
(

) 2
This is because the T 0 is free from the human error of T 1 .

Where: T, T 0 , P stand for defects threshold, automatic threshold, threshold correct constant, respectively.

Tab. Tab. [START_REF] Fu | Gray Level Transform Model of Ball Image and Its Application in Citrus Image Correction[END_REF] The correction constant P for surface defects extraction threshold

Results and Discussion

T 0 was obtained by processing the 268 images and divided into five intervals. One image was selected from each interval for the manual threshold, and the results are shown in Fig. 4. (1) The image processing results are shown in Fig. 4 which mainly include flaws and scratch. This processing the reflection of light effectively and simplify the image processing based on Near-infrared light to detect surface defects of kiwifruit. Using P and interval of T 0 could detect the defects of fruit. There was some difference in the extent of noise influence.

Results showed that interval of T 0 value from 0.1 to 0.2 had better effects than those of other intervals. Thus mathematical morphological operation was used to remove the noises.

(2) Using T 0 alone does not detect the surface defects for kiwifruit from Near-infrared image; however, it provided the precondition to quantify the standard of image segmentation reasonably, which meant that we could process different brightness images under the same standard. In Fig. 5, for example, the binary images of T 0 were almost the same though different intervals were selected.

(3) Compared with the results of image processing in [START_REF] Li | A Method for Surface Defects Detection in Kiwi Fruit Classification[END_REF], the results in this paper were much better as it overcame the influence of fruit calyx.

Conclusion

The function of Near-infrared light intensity and automatic threshold showed obvious linear relationship, with function fitting coefficient of R 2 > 95%. A linear mathematical model was developed to detect threshold of kiwifruit surface defects automatically.

In the five intervals of T 0 , the Near-infrared image processing excluded the interference of the fruit calyx and therefore this method was more effective to detect fruit defects of surface flaws and scratches, especially with T 0 range of 0.1 to 0.2.

Overall our data provide a promising tool for automatic defects detection in the fruit grading system.

Fig. 1 TFig. 2

 12 Fig.1 Kiwifruit Machine Vision Detect System

Fig. 3

 3 Fig.[START_REF] Feng | Study on Identifying Measurement about Default of Fruit in Computer Vision[END_REF] The relation between light intensity and T 0 、T 1 (Data in Tab. 2)

Fig. 4 -

 4 a is a Near-infrared original image, Fig.4-b is a threshold binary image of T 0 and Fig.4-c is the binary image of T 1 , Fig.4-d is the binary image of Model Threshold T.

4 )Tab. 5

 45 Fig.[START_REF] Fu | Gray Level Transform Model of Ball Image and Its Application in Citrus Image Correction[END_REF] Five image processing results

1

  Test conditions and image collection Put the scratch kiwifruits into lamp house; adjust the camera aperture according to Tab.1. Adjust the brightness coarse adjustment gears and fine adjustment gears in accordance with Tab.1. Finally collect images using supporting software Sapera Cam Expert and adjust R, G, B to 0 dB.

	Aperture (1.4~16.0) 1.4 3.7 4.0 6.0 8.0 8.0 12.0 16.0 adjustment gear 1 and fine adjustment 1~16 are recorded as 101,102,103, Coarse adjustment (1~16) Fine adjustment (1~16) Image quantity 1 3~11 9 2、3 1~16 32 3、4 1~16 32 5~10 1~16 96 5 1~16 16 6~16 1、9、16 33 6~16 1、9、16 32 8~16 1、16 18 104…116, while coarse adjustment gear 1~16 and fine adjustment 1 are Total (1) (2) Number the collected images and light intensity record mode. E.g. coarse recorded as 1601, 1602, 1603, 1604…1616.

2

  Part of the test data

	3	Results and Discussion
	No.	Aperture	Light intensity	Automatic Threshold T 0	Manual Threshold T 1
	001	1.4	103	0.0941	0.1400
	002	1.4	104	0.1333	0.2400
	003	1.4	105	0.1686	0.2590
	004	1.4	106	0.2039	0.3590
	005	1.4	107	0.2353	0.4000
	006	1.4	108	0.2706	0.4400
	007	1.4	109	0.3020	0.5300
	008	1.4	110	0.3333	0.5533
	009	1.4	111	0.3686	0.6700

3

  The relation between light intensity and automatic threshold

	No.	Aperture	Coarse adjustment	Fine adjustment	Function (T 0 =Ax+B)	R 2
	01	1.4	1	3~11	T 0 =0.0339x -3.3884	0.9992
	02	3.7	2	1~16	T 0 = 0.0057x -1.0295	0.9975
	03	3.7	3	1~16	T 0 = 0.0063x -1.7055	0.9985
	03	4.0	3	1~16	T 0 =0.0050 x -1.3321	0.9976
	04	4.0	4	1~16	T 0 =0.0052 x -1.8516	0.9935
	05	6.0	5	1~16	T 0 = 0.0027x -1.1500	0.9882
	06	6.0	6	1~16	T 0 = 0.0026x -1.3268	0.9893
	07	6.0	7	1~16	T 0 = 0.0027x -1.6486	0.9865
	08	6.0	8	1~16	T 0 = 0.0028x -1.9274	0.9927
	09	6.0	9	1~16	T 0 = 0.0029x -2.2458	0.9930
	10	6.0	10	1~16	T 0 = 0.0027x -2.2822	0.9808
	11	8.0	5	1~16	T 0 = 0.0011x -0.4516	0.9545
	12	8.0	6~16	1	T 0 = 0.0002x -0.0209	0.9995
	13	8.0	6~16	9	T 0 = 0.0002x -0.0109	0.9987
	14	8.0	6~16	16	T 0 = 0.0002x -0.0029	0.9993
	15	12.0	6~16	1	T 0 = 0.0001x +0.0099	0.9980
	16	12.0	6~16	9	T 0 =0.0001x + 0.0129	0.9981

Acknowledgments. This research was funded by The Project-sponsored by SRF for ROCS, SEM (KS08021101), Project by the National Natural Science Foundation of China (61175099) and Northwest Agriculture and Forestry University Talent Fund (Z111020902).

Foundation item: The Project-sponsored by SRF for ROCS, SEM (KS08021101), Project supported by the National Natural Science Foundation of China (61175099), Northwest Agriculture and Forestry University Talent Fund (Z111020902).


Zhenxiang Huang
email: zhenxianghuang@126.com

Bo Peng

Juan Wu

Keywords: gesture recognition, DTW, HMM, ANN, SVM, agriculture

With the gradual improvement of the computer performance and the use of computer more deeply in many fields,mouse and keyboard, the traditional human-computer interactive way,show more and more limitations. In recent years,gesture recognition interaction based on machine vision is used more and more widely due to its simple,nature,intuitive and non-contact advantages, which is becoming the research hotspot in the world. This paper mainly studies the main idea about DTW,HMM,ANN and SVM methods used in gesture recognition.This paper also expounds the basic model and future applications of gesture interaction system based on machine vision and research meaning of its application in agriculture.

Research and Application of Human-Computer Interaction System Based on Gesture Recognition Technology 1 Introduction

With the rapid development of information technology and gradual increase of computer's performance, human-computer interaction has become an important part in people's daily life when the use of computer is deepening in various fields.

The traditional human-computer interactive way,such as mouse and keyboard,shows more and more limitations,especially in emerging application fields of virtual reality, augmented reality and pervasive computing.The fact promotes the research on human-computer interaction technology develop towards the direction of people-oriented, free and direct manipulation.Gestures, playing an important role in the expression of a specific intent in people's daily life, are adopted as a new humancomputer interaction way in recent years. The gesture interaction based on machine vision has become the research focus of human-computer interaction due to its simple, natural, intuitive and non-invasive advantages.Gesture recognition is a technology which identifies a variety of gestures according to certain rules by computer , instructs the computer to translate into corresponding control commands or semanteme with the goal of computer operating or information exchange [START_REF] Shneiderman | Direct manipulation:A Step Beyond Programming Languages[END_REF] . The gesture interaction system based on machine vision input gestures through the acquisition module, in multocular case,cameras are distributed in accordance with a certain relationship in front of users [START_REF] Olsen | Where Will We Be Ten Years from Now[END_REF] ,in monocular case,it require the plane where the camera is and the plane of the user's hand movements should be in the same level [START_REF] Pavlovic | Visual Interpretation of Hand Gestures for Human-Computer Interaction:A Review[END_REF] .

Gesture Interaction System Model Based on Machine Vision

After reading the gesture images, interaction system detects gestures from the data stream on the basis of complexion and motion information [START_REF] Howe | Comparison of Hand Segmentation Methodologies for Hand Gesture Recognition[END_REF] ,then separates the gesture signal from the video signal and launch trajectory tracking.The CamShift algorithm [START_REF] Mahmoudi | Visual Hand Tracking Algorithms[END_REF] and Kalman filter algorithm are commonly used.In the gesture analysis stage, the analysis process includes feature extraction and model parameter estimation.In the identification stage, gestures are classified by the model parameters and generated description as required.At last,system drive the specific applications according to generated gesture description.

Main Algorithms on Dynamic Gesture Recognition

Trajectories matching method and state space modeling method are the main dynamic gesture recognition algorithms.The most representative trajectories matching algorithm is Dynamic Time Warping(DTW) algorithm, and the typical state space modeling algorithms include Hidden Markov Model(HMM), Artificial Neural Network(ANN) and Support Vector Machine(SVM).

DTW Algorithm

Human activities can be modeled by structures of high-dimensional temporal trajectories,gesture recognition can then be performed by measuring the similarity or the distance between the input gesture trajectories and existing gesture trajectories template from the training sample set [START_REF] Psarrou | Recognition of human gestures and behaviour based on motion trajectories[END_REF] . The Dynamic Time Warping(DTW)

algorithm is designed to exploit some observations about the likely solution to make the comparison between sequences more efficient. DTW algorithm assumes that the endpoints of two modes are aligned accurately,then it translates the matching problem into the problem how to use dynamic programming techniques to find the optimal path through the limited grid efficiently.The sequences are warped non-linearly in the time dimension to determine measure of their similarity independent of certain nonlinear variations in the time dimension. The advantages of DTW method are simple in concept, efficient and allow sufficient flexibility between the conceptual model and reference model [START_REF] Ren | Vision Based Recognition of Hand Gestures:A Survey[END_REF] .

HMM Algorithm

The state space modeling method is a algorithm that models for spatiotemporal characteristics of the gesture trajectories,it treats the trajectories as a series of transfer between the states. The specific approach is to learn the state transition parameters from training samples at first,then appraise the trajectories with different gesture models to achieve results. The typical algorithms include HMM,ANN and SVM. A Hidden Markov Model(HMM) is a double stochastic process model which is represented with parameters and used to describe probability statistical properties of stochastic processes.It includes the random process of the state transition and the random process of observation symbols output. It evolves from the Markov Chain.Markov Chain is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states. It is a random process characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. HMM can be regarded as a general form without the constraints of the Markov Chain [START_REF] Charniak | Statistical Language Learning[END_REF] . As a widely used statistical method,HMM that general topology structure has good ability to describe the spatiotemporal variation of the hand signals, but the result is uncertain due to HMM has more than one migration curve for a given output result,so it can't determine the state matrix of input set by output results. In addition, HMM can't defined migration rules accurately, for example, the number of states and the number and types of migration.It depends on priori knowledge and try to determine these rules. In fact, the migration mapping result of Markov Process isn't satisfactory and the topology structure of HMM is general. These all cause the model too complicated in analysis of hand signals and large calculation of training and recognition.

ANN Algorithm

An Artificial Neural Network(ANN) is a technology that imitates the structure and/or functional aspects of biological neural networks by advanced engineering technologies. Its purpose is to enable the robot to perceive,learn and reasoning like human brain. ANN consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. BP Neural Network Model is used most widely in recent years. It is a multilayer feedforward neural networks of one-way transmission, have unilaminar or multilayer hidden layer nodes except input nodes and output nodes, no coupling among nodes of the same layer, input signal pass through all layer nodes in turn from input layer nodes to output layer nodes. Output results of each layer nodes only affect output results of the next layer nodes.BP Neural Network Algorithm has characteristics of self-organizing and self-learning for information processing, has strong ability of anti-noise, model spread and processing incomplete models.Its main defect is low convergence rate and unavoidable overfitting phenomenon.

SVM Algorithm

A Support Vector Machine(SVM) is a machine learning method based on Statistical Learning Theory(SLT), primarily research how to get the best results from small sample.In other word, it seeks a optimal balanced scheme between complexity of model and learning ability on the basis of limited sample information [START_REF] Cristianini | An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[END_REF] . It solves practical problems well, such as small sample, nonlinearity, over learning, highdimension pattern recognition and local minima, by the method of structural risk minimization principle and the kernel function. Due to its success in dealing with the relationship between the dimensions and computed strength and enhancing the computational efficiency greatly,SVM plays an important part in real-time dynamic gesture recognition.

Gesture Recognition Interaction Application in Agriculture

As a new human-computer interactive way that different from traditional interface, the characteristics of gesture recognition interaction like intuitiveness and naturalness, detemines its good application prospects in many fields,for example,in agriculture.

In the virtual agricultural field,operating objects in virtual environment by gestures can simulate the plant shape that crops may reach maximum yields accurately, imitate crop row spacing of intercropping and companion planting intuitively. These can provide useful reference for planning cultivated land reasonably. By the simulation of virtual agriculture,learning of agricultural technology will be visualization, interaction will be realistic,then the agricultural practitioners will understand and master agricultural knowledge better.

In the field of agricultural production, gesture recognition interaction will lower the use barrier of computer,which will help farmers to use intelligent systems for agricultural production conveniently.It also can control agricultural robot in long distance for collection or fishing in extreme environments,such as poor weather conditions [START_REF] Wang | Research and Implementation of the Object-Oriented Virtual-Hand Technology[END_REF] .

Gesture recognition also can be used for interaction of information display system in public places including agricultural production site where mouse and keyboard aren't suitable to be used [START_REF] Chen | Interacting with Digital Signage Using Hand Gesture[END_REF] . In addition, gesture recognition interaction can be used for distance education system and video conference system to facilitate agricultural production and spread last agricultural information and technologys [START_REF] Mitra | Gesture Recognition:A Survey[END_REF] .

Gesture Recognition Interaction Application Prospects

With the help of gesture recognition technology, remote control of household appliances such as TV, DVD, stereo can be achieved and provide convenience for people's life [13][14] ,game operation will also be more natural and game playing experience will be enhanced.

Gesture recognition technology can be used for sign language translation system [START_REF] Yang | Extraction of 2D motion trajectories and its applicatio n to hand gesture recognition[END_REF] ,then deaf-mute people can communicate with computer and normal human by it,which will improve their education level and provide convenience for two-side communicate.

With the development of gesture recognition technology, mouse and keyboard being replaced in the near future will not be a dream only, which will reduce the cost of human-computer interaction and e-waste, be consistent with the historical trend of green low-carbon.

Conclusion

As an important development direction of new generation human-computer interaction system, hand gesture recognition has raised widespread concern of researchers and have achieved certain results. Gesture recognition technology combined with agriculture informationization is good for the promotion of intelligent agricultural production equipment, rapid popularization of agricultural technology and fine control of agricultural production. These will significantly reduce production costs, increase productivity and make an important contribution to world food security.

Fig. 1 .

 1 Fig. 1. Basic gesture interaction system model based on machine vision


Qiaoxue Dong

Weizhong Yang

Lili Yang

Yifei Chen

Shangfeng Du

Li Feng

Qinglan Shi

Yun Xu

Crop Model-Based Greenhouse Optimal Control System :Survey and Perspectives

Keywords: Greenhouse optimal control, Crop growth model, Greenlab model, Dynamic optimization, Energy saving

A survey is presented of the developments in scientific literature on the greenhouse optimal control. The related problems to optimal control were discussed, and the schematic diagram of optimal control system, combined crop models and optimization algorithm were covered focusing on issues such as: model simplification and validation, definition of objective function, input or output constraints and dynamic optimization, and especially structure-function Greenlab model was made s a first exploration for the optimal control application. Not only the above research issues were listed in the paper, the perspectives and the solutions are presented as well. It is pointed out that only the crop growth model is integrated into the greenhouse climate optimal control, the best economical result and energy-saving can be warranted.

Introduction

The operation cost of current modern greenhouse is usually high due to the complexity of greenhouse-crop production system, so many efforts have been made to develop optimal management and control strategy which aims to reduce the energy consumption, thus to improve the efficiency of greenhouse production [START_REF] Van Straten | Towards user accepted optimal control of greenhouse climate[END_REF][START_REF] Ioslovich | Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop[END_REF][START_REF] Van Henten | Improving the efficiency of greenhouse climate control: an optimal control approach[END_REF][START_REF] Chalabi | A real-time optimal control algorithm for greenhouse heating[END_REF][START_REF] Dieleman | Energy saving in greenhousees: optimal use of climate conditions and crop management[END_REF][START_REF] Blasco | Model-based predictive control of greenhouse climate for reducing energy and water consumption[END_REF]. Simulation and experimental work were also carried out to support the effectives of optimal control system. For instance, Van Ooteghem found that through the simulation of a optimal closed loop controls, the boiler use was reduced, thereby reducing fossil energy use, and gas use was decreased by 77% compared to a conventional greenhouse [START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF]; Van Henten made a experimental comparison of the performance between optimal control approach with the traditional control system, and the results showed that with optimal control , energy and carbon dioxide are used more efficiently [START_REF] Van Henten | Improving the efficiency of greenhouse climate control: an optimal control approach[END_REF].

However, it is still not an easy task to apply the optimal control concepts into greenhouse production practice , because the current models for greenhouse and crop are usually too complex to be incorporated into the optimal control. But crop models are essential, and without the support of crop models, it will be difficult for optimal control system to achieve the appropriate results, because the dynamic greenhousecrop models output the state variables required by objective function [START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF][START_REF] Lentz | Model applications in horticulture: a review[END_REF]. The quality of crop models will affect the preformance of the optimal control , so for application of optimal control, accurate and simplified models are required , i.e. Only if to build an optimal control system combined appropriate crop growth model, energy efficient operation can be implemented [START_REF] Van Straten | Towards user accepted optimal control of greenhouse climate[END_REF]. In this paper, we will give a brief description about such optimal control system, and its recent advances about related optimal control problem will be presented in detail in order to exploit the possibilities for practical application.

2

The System Structure of Crop Model-Based Optimal Control

In order to understand the system structure of optimal control, in this section we will illustrate it by first comparing with the conventional climate control system in current practice (Fig. 1). In conventional control of the greenhouse climate, the grower defines the set-points of the greenhouse climate variables such as temperature, humidity and carbon dioxide concentration , and the climate control computer works to achieve the desired climate using measurements and feed-back control techniques. During the growth season, the grower may modify the setting trajectory based on the observation of crop growth state and their experience. In the above traditional system, trajectory settings were determined by the grower according to heuristic rules, which will have a definite effect upon the consumption of energy and other resources, as well as on growth and development of the crop, but the exact effect is unknown to the grower, and can at present only be inferred from experience [START_REF] Van Straten | Towards user accepted optimal control of greenhouse climate[END_REF].

We can see that the traditional control system of greenhouse climate don't take into account the costs in an explicit manner, and provide the control actions by focusing almost always on the performance. So the costs for keeping that performance might be unacceptable [START_REF] Blasco | Model-based predictive control of greenhouse climate for reducing energy and water consumption[END_REF].

The optimal control strategy provide an alternative method to consider energy consumption [START_REF] Dieleman | Energy saving in greenhousees: optimal use of climate conditions and crop management[END_REF][START_REF] Blasco | Model-based predictive control of greenhouse climate for reducing energy and water consumption[END_REF]. It consists of models for the greenhouse as well as for the crop, a suitable objective function and an optimization algorithm(Fig. 2), which will be introduced in the following.

In Fig. 2 system, the control inputs are not the settings defined by the grower like above conventional system, because the incorporation of the heuristic control rules in the optimal control decreases the optimal control freedom. Therefore the greenhouse -crop model used in this research is based on actuator values as control inputs. But the input and output constraints are still need to be given by the grower. Other inputs include external weather data, and because the results of the optimal control strongly depend on the weather conditions, reliable forecasts are also needed in the optimal control system.

Fig. 2.

The system structure of model-based optimal climate control Optimal control approach is based on a mathematical model for calculating greenhouse energy consumption and on a mathematical method for minimizing total energy consumption, so the models for greenhouse climate as well as the crop are required. In a formal way, the model is usually represented by state equation [START_REF] Van Henten | Improving the efficiency of greenhouse climate control: an optimal control approach[END_REF] The function (.) f describes the dynamic behavior of greenhouse climate and crop, i.e. modeling methodology. In most literatures about optimal control strategy, the greenhouse climate model used is mainly based on energy and mass balance equations [START_REF] Chalabi | A real-time optimal control algorithm for greenhouse heating[END_REF][START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF] , the details of which will not be explained in this paper, and the description about the energy balance can be found elsewhere [START_REF] Jollie | HORTICERN: An improved static model for predicting the energy consumption of a greenhouse[END_REF][START_REF] Van Bave | Global comparison of three greenhouse climate models[END_REF][START_REF] Bot | Greenhouse climate: from physical processes to a dynamic model[END_REF]. While the crop growth model used in present optimal greenhouse control will be described in detail in section 3.

,i.e. b b x t x t v u x f dt dx   ) ( ), , , , ( (1
The objective function in the optimal system Fig. 2 summarizes the output variables to one single performance criterion, which depends on the requirement of user, and it will affect the performance the optimal control. In general, the performance criterion can be summarized into two category: (1) economic performance criterion; (2) cost function. According, the definition of objective function is

    f b t t f f dt t v u x L t t x u J ) , , , ( ) ), ( ( ) ( (2)
Or      f b t t f f dt t v u x L t t x u J ) , , , ( ) ), ( ( ) ( (3)
Where (.)

 is the term related with the expected crop economic value and (.) L term related with the operation costs of the climate conditioning equipment. The optimal control problem defined by Eq. 2 is to determine the trajectory of the control inputs

) ( * t u over ] , [ f b t t
which maximize the economic revenue or production, while the performance criteria defined by Eq.3 aims to minimize the total energy consumption subjected to input and output constraints.

The optimization method is to find the solution of above optimal control problem defined by the objective function. In section 4, several commonly used optimization algorithm will be discussed and reviewed briefly.

Greenhouse Crop Model Oriented For Optimal Control

The Model quality is a fundamental aspect to achieve adequate control performances. Here some successful crop models in horticultural practice will be discussed in order to exploit their possibilities applied in the optimal control. At present, the main obstacles are that the current greenhouse crop models are too complex, or require too many data or too much calculation, which is not suitable for optimal control task, so simple or very compact models are required.

Current horticultural crop models are mostly based on a photosynthesis model and an evaporation model, and the typical instance is TOMGRO [START_REF] Jones | A dynamic tomato growth and yield model (TOMGRO)[END_REF][START_REF] Dayan | Development, calibration and validation of a greenhouse tomato growth model:I. Description of the model[END_REF]. Although TOMGRO model has been validated and proved enough accurate , it has too many parameters to be directly involved into the optimal control method . So its simplified version is proposed in order to be suitable for the optimal application . For instance , Pucheta restricted TOMGRO model to two state variables : dry weight and number of leaves which are modeled by Eq.4. to implement the optimal greenhouse control of tomato-seedling crops [START_REF] Pucheta | Optimal greenhouse control of tomato-seedling crops[END_REF].

) ( ], ) ( ) , , ( [ 2 T r r N W T R CO S T P E W m m PAR g      (4)
Where W and N are the time-dependent state variables representing the total dry weight and the number of leaves, respectively.

(.

)
g P is the photosynthesis rate which depends on the temperature , solar radiation and the concentration of CO 2 ;

(.) m R is respiration rate of the leaves, which only depends on temperature.

van Straten also described a simplified model which has similar modeling mechanism with the description in Eq.5 , but it gives a fairly general representation of crop production [START_REF] Van Straten | Towards user accepted optimal control of greenhouse climate[END_REF].

(

) 5
Where Above models is only a general description for optimal control-oriented application. Ioslovich fruther proposed a three-stage growth model for tomato named MBM-A [START_REF] Ioslovich | Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop[END_REF], which is modelled in a more adequate and accurate way . In MBM-A model, two state variables namely accumulated vegetative dry mass x and the harvestable red fruits y are defined and they have different differential equations as described by Eq.6.

                 stage ve reproducti x f U t M dt dy dt dx stage ve reproducti vegetative K y g U t M dt dy x f U t M dt dx stage vegetative dt dy x f U t M dt dx c   ) ( ) , ( , 0 / ) ( ) , ( ), ( ) 1 )( , ( 0 ), ( ) , ( (6)
From Equation 6, the time-dependent state variables x and y relies on control variablesU , including greenhouse heating, ventilation and CO2 enrichment, which will influence the greenhouse climate such as temperature and CO2 concentration.

Further assumption is that the mean daily temperature is strongly correlated with the mean daily light, and light is strongly correlated with photosynthesis. In Eq.6, unknown coefficients are need to be calibrated, and the model was validated against TOMGRO.

The recently emerged plant structure-function model Greenlab also provides the possibilities to be introduced into the optimal control strategy [START_REF] De Reffye | Relevant qualitative and quantitative choices for building an efficient dynamic plant growth model: GreenLab Case[END_REF]. Because it is an efficient dynamic process of balancing the simplicity and complexity. It consists of biomass production and allocation equations, and the plant produces biomass by leaf photosynthesis,i.e.

)] ) ( ) ( 2 exp( 1 [ 1 ) ( ) ( )) ( ), ( , 2 ( , 1 ), ( ), ( ( ) ( ) ( 1 i S i S r r i S i E i N i S r g r i E i S F i Q i N j j       (7)
where

) (i
Q represents the dry biomass created at the growth cycle i and we define the growth cycle (GC) as the thermal time necessary for each plant axis to develop a new growth unit (GU); j S is the blade area of the j th leaf, and ) (n

S

is a kind of ground projection area of the leaf surface; Parameter 1 r sets the leaf-size effect on transpiration per unit leaf area, while 2 r accounts for the effect of mutual shading of leaves according to the Lambert-Beer's law.; ) (i N is the number of leaves;

) (i E is the average biomass production potential depending on environmental factors, such as light, temperature and soil water content.

The biomass produced by photosynthesis is redistributed among all the organs according to their demands and sink strength:

) 1 ( ) ( ) ( ) , (     i Q i D j f P j i q o o o (8)
Where o q  is the biomass increment of o-type organ; o P are the organ sink strengths and are model hidden parameters.

f are normalized distribution functions characterizing the evolution of the sink strengths;

) (i D is the total biomass demand.
The Greenlab model provides an interaction interface with climate and an explicit definition about growth cycle(the duration of which can vary from several days(tomato) to one year), so it describes the evolution of the plant structure periodically. Furthermore, the validation study of this Greenlab model has been well made [START_REF] Wang | Tomato growth modeling based on interaction of its structure-function[END_REF][START_REF] Zhan | Architectural modeling of wheat growth and validation study[END_REF] , and the research work shows possible applications of GreenLab in optimization and control for agronomy.

Optimization Algorithm

The Effective optimization methods play an key role on finding the solution of optimal control problem. The above-stated control problems in section 2 belong to dynamic optimization problems which can be solved by choosing a dynamic programming technique, such as Hamilton-Jacobi-Bellman equation [START_REF] Ioslovich | Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop[END_REF], Lagrange Multiplier technique [START_REF] Bertsekas | Constrained optimization and lagrange multiplier methods[END_REF], Genetic algorithm [START_REF] Wu | A water supply optimization problem for plant growth based on GreenLab model[END_REF], Model-based Predictive control [START_REF] Blasco | Model-based predictive control of greenhouse climate for reducing energy and water consumption[END_REF]and Receding Horizon Optimal Controller [START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF], and so on.

However, optimal control concepts and nonlinear dynamic programming (NDP) in particular have almost not been used in practice, due to the implementation complexity. Therefore the researchers try to improve the optimization techniques mentioned above to be capable of using crop growth models. For example, Luus proposed iterative dynamic programming(IDP)procedure to solve the optimal control problem [START_REF] Luus | Luus-Jaakola Optimization Procedure[END_REF]. IDP-a modified dynamic programming is a computational procedure that allows one to obtain the optimal control trajectories for time varying nonlinear processes, with any type of performance indices and with restrictions on both state and control variables. The application of this algorithm to a continuous system requires discretization of the differential equations that model the process, and quantification of the variables of state, decision, control and time. Ioslovich used the Hamilton-Jacobi-Bellman formalism in the form of Krotov-Bellman sufficient conditions and the optimal value of the constant seasonal control intensity can be approximately obtained the MBM-A model by Eq.3 [START_REF] Ioslovich | Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop[END_REF]. Because during each sampling period, the simulation of the associated optimal closed loop control system is very time consuming in general. to drastically limit the simulation time, the optimal control problems can be solved by the receding horizon optimal controller [START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF][START_REF] Wu | A water supply optimization problem for plant growth based on GreenLab model[END_REF] used genetic algorithm combined with Greenlab model to achieve the simulation for greenhouse water supply optimization problem, which open the possibilities for Greenlab model to be applied to optimal greenhouse climate control .The advantage of genetic algorithm is to avoid local minima.

Optimal control problem based on non-linear and non-quadratic performance criteria, such as those discussed in this paper , are very difficult to solve analytically. Iterative schemes need to be used to achieve a numerical solution of the mathematical problem. Dynamic optimization problems can be numerically solved by direct or indirect methods. Over the last years, a number of numerical search methods have been developed. For instance, Van Henten proposed a modified steepest ascent algorithm and used a forth order Runge-Kutta integration algorithm to obtain the numerical solution for optimal control trajectories [START_REF] Van Henten | Improving the efficiency of greenhouse climate control: an optimal control approach[END_REF]. An indirect gradient method was proposed which has proven its effectiveness in optimal greenhouse control and many other fields [START_REF] Van Ooteghem | Receding horizon optimal control of a solar greenhouse[END_REF][START_REF] Van Willigenburg | Three timescale receding horizon optimal control in a greenhouse with a heat storage tank[END_REF]. The search procedure to find optimal value is described as the following:  start the simulation model with initial values for a set of input variables;  calculates the output variables by the simulation model and an objective function value;  derives an improved set of controls the optimization procedure and starts a new simulation procedure The above feedback loop is repeated until the objective function value converges on the optimum .Since the simulation model runs independently from the optimization algorithm, there are in principle no restrictions with respect to its structure. The drawback is ,there is no certainty finding the absolute minimum or maximum [START_REF] Lentz | Model applications in horticulture: a review[END_REF]. Besides this, the selection of initial values also need to paid more attention because it will affect the performance of optimal algorithms.

Discussion and Perspectives

This paper presented a framework of crop model-based optimal greenhouse control system and key factors, which shows optimal control of the greenhouse is feasible. However, even if some valuable research work has been made during the last decades, we can see that it is still a long way for the optimal control strategy to be applied into greenhouse practice, especially for China horticulture production. To solve above problem, current elaborate and complex horticultural crop models are needed to simplified to be suitable for optimal control, and only if are simplified models calibrated and experimentally proven accuracy, they can be incorporated into the optimal algorithms; Efficient optimal algorithms are required to solve the timeconsuming and stability problem, and time-scales problem induced by crop growth response and control actions are also the obstacles to hamper the application of optimal control. Despite the challenging list of issues that need further investigation, we are convinced that with the improvement of modeling technology and computer power, the optimal control methodology based on crop growth model must find its way for on-line production practice, and provide an effective tool to achieve the energy saving.

Fig. 1 .

 1 Fig.1. A schematic diagram of the conventional climate control procedure[START_REF] Van Henten | Improving the efficiency of greenhouse climate control: an optimal control approach[END_REF]

)

  in which x are the state variables, u are the control inputs, v are the external inputs, t denotes time and dt dx represents the rate of change of the state in time.The initial state of crop production process is denoted by ) ( date. The state of production process is represented by variables relating to crop such as fresh weight, as well as to variables describing the indoor climate such as air temperature, humidity and carbon dioxide concentration.

  two state variable representing not-structural biomass and structural biomass.C , I and T are the temperature , solar radiation and carbon dioxide concentration, respectively.

Corresponding author: llyang@cau.edu.cn, dongqiaoxue@163.com

Acknowledgments. This work is supported in part by Natural Science Foundation of China (#61174088) and Fundamental Research Funds for CAU(#2012QJ078).


Shaoxin Guo

Fan Zhang

Jianqin Wang

Cold Chain Logistics Monitoring System with Temperature Modeling

Keywords: cold chain logistics, ARM, temperature modeling

Cold chain logistics guarantees the quality and freshness of some especial agriculture products. At present cold chain logistics monitoring system can just monitor single point of the temperature in the cold chain vehicle body, it cannot estimate the space temperature distribution of cold chain vehicle body. This paper introduces a cold chain logistics monitoring system based on ARM Linux. The data of temperature value in the cold chain vehicle body and the geographic location of cold chain vehicle will be monitored in real time. This paper presents a space temperature modeling method based on mutli-point temperature value to estimate the space temperature distribution of cold chain vehicle body.

Introduction

The specific characteristics of some special agriculture products like fish, egg, meet and vegetables determined the logistics of those agriculture products must be in low temperature. So the definition of cold chain logistics is based on freezing technology, by the method of refrigeration technique to guarantee the freshness and quality of agriculture products [START_REF] Dong | China's agricultural products Cold-chain logistics status, Problems and Solutions[END_REF] .

In the developed country such as USA and Japan, cold chain logistics system was built with the core of information technology, by the use of storage technology, transportation technology, distribution technology, loading and unloading technology, and stock control technology [START_REF] Liu | Summary of Development Pattern and Government Behavior of Cold Chain Logistics of Agricultural Products[END_REF] .

In recent years by the establishment of the cold chain logistics grid, cold chain logistics have been covered in everywhere of China. But currently there are still some problems of cold chain logistics in China. The problem is the facilities of cold chain logistics is not perfect, the low level of Market-oriented and the competitive environment of cold chain logistics companies is very bad due to the low price competition [START_REF] Wu | The Current Situation and the Countermeasures of China's Cold Chain Logistics Development[END_REF] .

The most important key to solve the problem is to build the facilities of cold chain logistics, to enhance the management of cold chain logistics companies, to build the standard of cold chain logistics. Additionally the management of the cold chain logistics should be within information technology [START_REF] Wu | Comparative Study on the Development Levels of Domestic and International Cold Chain Logistics for Agricultural Products Based on AHP[END_REF] .

So the technologies and facilities for cold chain logistics are necessary for the development of the modern cold chain logistics [START_REF] Zhang | Current situation, development trend and countermeasures of aquatic products cold-chain logistics technology[END_REF] . Therefore, to build a cold chain logistics monitoring system is the way to enhance the efficiency of management in cold chain logistics.

Li and You proposed a temperature tracking system based on iButton-DS1923 [START_REF] Li | Cold chain recording instrument of temperature and humidity tracking based on iButton-DS1923[END_REF] , but this system cannot estimate the temperature distribution of the cold chain vehicle body and cannot locate the cold chain vehicle. Wang proposed a temperature monitoring system based on RFID [START_REF] Wang | RFID-based temperature monitoring system of frozen and chilled tilapia in cold chain logistics[END_REF] . But this system cannot monitor the temperature in real-time.

This paper introduces a cold chain logistics monitoring system with temperature modeling. There are two sub-systems of this monitoring system, one is embedded system and the other is control center. Embedded system collects the data about temperature value of cold chain vehicle body and the geographic location of the vehicle, and then sends the data to control center in real-time. The program run in control center receives the data from embedded system, draws the graph of temperature modeling and also shows the geographic location of the cold chain vehicle.

The definition of temperature modeling in this paper is based on the method of interpolation to estimate the temperature distribution in the cold chain vehicle body. There are some interpolation method is widely used. Those methods are distance weighing, interpolating polynomials, Kriging and spline method [START_REF] Lin | Comparison of Three Spatial Interpolation Methods for Climate Variables in China[END_REF] . In the group of the interpolation method, the distance weighing method is most convenient, but huge error will occur. Spline interpolation method is based on some extreme point, by the way of control estimate variance to get the smooth spline by polynomial. But it is not fit to temperature modeling, because the calculation is huge. The Kriging interpolation can get the optimum linear unbiased estimate, but Kriging interpolation needs many interpolation points. In the temperature modeling system the number of interpolation point is limited, so Kriging interpolation is not fit to temperature modeling. The space interpolation method for temperature modeling should not only more convenient and precise, but also less calculation.

Because of the continuity characteristic of temperature distribution, this paper introduces a temperature modeling method based on three dimensions Lagrange interpolation [START_REF] Feng | Multivariate Polynomial Interpolation[END_REF] . Within the temperature modeling method, the temperature in the cold chain vehicle body will be estimated. So that it is very convenient to monitor the temperature distribution in the cold chain vehicle body.

Design of Embedded System

This embedded system was installed in the cold chain vehicle, the core of the embedded system is a S3C6410 MCU, and it is a 32-bit ARM11 RISC microprocessor. The memory in this embedded system is 128MB DDR RAM and 1GB NAND FLASH. The MCU controlled 8 temperature sensors, which installed in the 8 vertex of the cold chain vehicle body. GPS module is used to get the cold chain vehicle geographic location. GPRS modem in embedded system is used to communicate with control center. Figure 1 shows the structure of embedded system. S1 to S8 means temperature sensor No1 to No8. The operating system of embedded system is Linux. Linux is famous for its efficiency flexibility portability and stability. The file system in the embedded system is yaffs2. The application run in the embedded system is to get the temperature value from temperature sensor and cold chain vehicle geographic location from GPS module, and then transfer the data to control center by GPRS modem.

The Method of Temperature Measurement

The temperature sensor DS18B20 is used to measuring the temperature, it can just use one wire bus to communicate with the MCU [START_REF]Maxim Integrated Products[END_REF] . The 8 DS18B20 connected to the S3C6410 GPIO port. The Linux driver is used to receive the data from DS18B20. The Linux drivers are like an interface of hardware and Linux kernel. There are two sort of device controlled by Linux driver, char device and block device. The DS18B20 is controlled as char device by the Linux driver. The process of read temperature value is shown in table 1.

Table 1. The process of receive temperature value from DS18B20

Step Function 1

Open device 2

Select which ds18b20 3

Reset the ds18b20 4

Send skip check rom command [0xCC] 5

Send convert temperature command[0x44] 6

Reset the ds18b20 7

Send skip check rom command [0xCC] 8

Send read data register command[0xBE] 9

Read temperature data

The Method of Vehicle Location

GPS module is used to locate the cold chain vehicle. GPS (Global Positioning System) is a space satellite navigation system which can provides location almost anywhere in the earth and time information. The GPS module connected to the embedded system through serial port. GPS module send the data include UTC time, longitude, latitude and other information to the MCU through serial port. The program just analyze the frame first with $GPGGA. This frame is most important in GPS, it include most geographic position information, and this frame used very widely. Based on the NMEA-0183 protocol the format of $GPGGA frame is shown in table 2 [START_REF] Joong Yoon | UGV (Unmanned Ground Vehicle) Navigation Method using GPS and Compass[END_REF] . The process of get data about latitude and longitude is first open the device of serial port (/tty/SAC1), and then set baud rate 9600, using read() function to read the string of GPS module send, and then using the sscanf() function to match the GPGGA frame and get the data about latitude and longitude.

The Method of Real-time Data Transmission

GPRS modem is used to transfer the data between embedded system and control center. GPRS (General packet radio service) is a packet oriented mobile data service on GSM mobile phone. Because of the packet communication technology is used in GPRS system, the usages of network resources will be greatly optimized. GPRS network supports the TCP/IP protocol, and it can connect to the X.25 network [START_REF] Zhai | GPRS/GPS/GIS Based Vehicle Navigation and Monitoring System[END_REF] . The data transport rate of GPRS is ranged from 40Kbps to 100Kbps [START_REF] Xia | Wireless Meter Reading Based on GPRS[END_REF] . In China GPRS network has almost covered everywhere.

GPRS modem connects to Internet base on PPP protocol by AT command. PPP (point-to-point protocol) is the protocol about data transmission between two nodes of network. The link that the protocol creates is Full-duplex. The PPP protocol transport data through the point to point link. PPP protocol packaging the IP datagram and combine the datagram in GPRS packet, finally GPRS modem send the packet off. The merit of PPP is used easily and widely also it supports user verification and IP distribution.

PPP protocol is consists of three parts [START_REF] Liu | Design and Implementation of PPP in uC/OS-II Operating System[END_REF] : 1. High-Level Data Link Control (HDLC) protocol used to package IP packet to link for transmission. 2. Link Control Protocol (LCP) used to set up PPP communication. 3. Network Control Protocol (NCP) that is run atop of PPP and used to negotiate options for a network layer protocol running atop PPP.

The process of the establishment about PPP link is shown in figure 2. When the PPP link is established, socket API is used to send and receive data in software layer. Socket API is provided both in Linux and Windows. Control center bind the process of monitoring program to the port, and listening socket request and accept the request. The program in embedded system call the send function to send the data to the control center and the program in control center call the recvfrom function to receive the data.

Space Temperature Modeling

Three Dimensions Lagrange Interpolation

In order to estimate the temperature in the space of the cold chain vehicle body by the temperature sensor installed in the 8 vertex of the cold chain vehicle body. The method of three dimensions Lagrange interpolation is used. The three dimensions Lagrange interpolation is based on one dimension Lagrange interpolation.

suppose that in three-dimensional space {(x,y,z)|x∈[a,b],y∈[c,d],z∈[e,f]},and it was divided in :

01 .... m a x x x b      (1) 01 .... n c y y y d      (2) 01 .... l e z z z f      (3)
So the m-th power Lagrange basis function about x is: 0 ()

m t i it t ti xx lx xx       (4)
The n-th power Lagrange basis function about y is: 0 ()

n t j jt t tj yy ly yy       (5)
The l-th power Lagrange basis function about z is:

0 () l t k kt t tk zz lz zz       (6)
So the three-dimensional Lagrange basis function is the product of each basis function:

( , , ) ( ) ( ) ( )

ijk i j k l x y z l x l y l z  (7)
Finally the Lagrange interpolation polynomial is:

                         l k t t n j t t m i t t m i n j l k k j i ijk m i n j l k k j i t k t t j t t i t z z z z y y y y x x x x z y x f z y x l z y x f z y x P 0 0 0 0 0 0 0 0 0 ) , , ( ) , , ( ) , , ( ) , , ( (8)
Can prove that the equation ( 8) is unique [START_REF] Li | Polynomial Interpolation Problem in R~S Space[END_REF] .In this polynomial f(x i , y j , z k ) is the temperature sensor value in the direction vector(x i , y j , z k ) of the cold chain vehicle body. The direction vector (x,y,z) is the point that the temperature to estimate. And the P(x,y,z) is the point(x,y,z) estimated temperature.

Experiment of Space Temperature Modeling

Suppose that the length of the cold chain vehicle body is L, the width of the cold chain vehicle body is W, the height of the cold chain vehicle body is H. In this experiment L is 5.13 meters, W is 2.91 meters and H is 3.86 meters. The temperature sensor parameter about location and temperature value is shown in table 3. By the use of three-dimensions Lagrange interpolation, the temperature value in anywhere of cold chain vehicle body can be estimated. Figure 4 shows the graph of both temperature ruler and temperature distribution.

Conclusions

This paper proposed a cold chain logistics monitoring system with temperature modeling, and the experiment of this system indicates that: 1. By method of three dimensions Lagrange interpolation, the temperature in anywhere of the cold chain vehicle body can be estimated accurately. 2. The information includes latitude and longitude of cold chain vehicle is also obtained. It reflects the geographic location of the cold chain vehicle. 3. The transmission of all the data is in real-time.

So that the manager in the cold chain logistics company just sit in front of the control center computer he can view the information about the temperature distribution and the geographic location of each cold chain vehicle in the company. And the efficiency of the management in cold chain logistics company will be enhanced by this cold chain logistics monitoring system.

Fig. 1 .

 1 Fig. 1. The 8 temperature sensors, GPS module and GPRS modem are connected to the embedded system.

Fig. 3 .

 3 Fig. 3. The position of the 8 temperature sensors and the value of each temperature sensor are displayed.

Fig. 4 .

 4 Fig. 4. The temperature ruler and temperature distribution are displayed. If the temperature is near 5 Celsius degrees the color turn to yellow, and if the temperature is near 3 Celsius degrees the color turn to green.

Table 2 .

 2 The format about GPGGA frame.

	$GPGGA,<1>,<2>,<3>,<4>,<5>,<6>,<7>,<8>,<9>,M,<10>,M,<11>,<12>*xx<CR><LF>
	<1>	UTC time (hhmmss.sss)
	<2>	Latitude (ddmm.mmmm)
	<3>	Hemisphere of latitude (N or S)
	<4>	Longitude (dddmm.mmmm)
	<5>	Hemisphere of Longitude (E or W)
	<6>	State of GPS
	<7>	Number of Satellite (00 to 12)
	<8>	horizontal dilution of precision (0.5 to 99.99)
	<9>	Altitude (-9999.9to9999.9meters)
	<10>	Height of geoid above WGS84 ellipsoid
	<11>	Time since last DGPS update
	<12>	DGPS reference station id

  There are 4 steps to establish the PPP link. Step 1 is link establish stage. Step 2 is user verification stage. Step 3 is network-layer protocol stage. And step 4 is link termination stage.

		NO
	Link Dead	Link Establish Stage	Link established successful?	YES	Verification Stage
		Link Termination Stage	Network-Layer Stage	YES	Verification successful?
			NO
	Fig. 2.

Table 3 .

 3 The location and temperature value about the 8 temperature sensors.The 8 temperature sensors was installed in the 8 vertex of the cold chain vehicle body. The temperature sensors in each vertex of cold chain vehicle body and its value are shown in figure3.

	ID	Location	Value
	1	(0,0,0)	4.11
	2	(0,0,L)	3.16
	3	(0,W,0)	4.67
	4	(0,W,L)	4.12
	5	(H,0,0)	3.10
	6	(H,0,L)	4.95
	7	(H,W,0)	3.34
	8	(H,W,L)	4.23

Acknowledgments: This paper is supported by Chinese Universities Scientific Fund (2012QT003).