Research of Video Surveillance and Diagnosis System for Plant Diseases Based on DM6446 Wang Xiuqing 1, a Qie Xu 1, b Zhang Chunxia 1, c Zhao Na 1, d

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1 Applied Mechanics and Materials Online: ISSN: , Vols , pp doi: / Trans Tech Publications, Switzerland Research of Video Surveillance and Diagnosis System for Plant Diseases Based on DM6446 Wang Xiuqing 1, a Qie Xu 1, b Zhang Chunxia 1, c Zhao Na 1, d 1 No.1038, Dagunan Road, Hexi District, Tianjin, China a lwxqly@163.com b love.huiyang@163.com c zhangchx@tust.edu.cn d Keywords: video coding; streaming media technology; video surveillance; neural networks; diagnosis of plant disease Abstract. TMS320DM6446 was used as a platform to build a video monitoring and controlling system for plant diseases. TMS320DM6446 can capture and encode the plant video. It delivered the video stream to the PC monitoring center by using streaming technology. Monitoring center displayed the received video, monitored the plant condition of growth and disease, and then obtained the image and relevant characteristics of disease. It used a combination of neural networks and expert systems to diagnose the plant diseases, and gave the control methods. The experimental results show that the proposed system can timely collect information about plant growth status and disease, diagnose the type of disease correctly and then reach the purpose of effective prevention and control for plant diseases. Introduction In the agricultural production, plant diseases and insect pests, have always been big basic problems for the plant growth. They can make a decline in production and quality, and ultimately lead to the farmers' economic damage. Due to the wide use of chemical fertilizers and pesticides for a long time, it brings a series of ecological problems, and then threatens human health directly [1]. Image of pests and diseases can reflect the growth and disease situation of plants objectively and truthfully. At present, the application of image monitoring system in the agriculture of China is relatively weak. The main way is to take pictures by digital camera in the fields, and then deliver the photos to a server by mail. But it is poor timeliness and labor-intensive [2,3]. TMS320DM6446 (hereinafter referred to as DM6446) is based on DaVinci technology, is a SOC chip of dual-core architecture integrated with DSP and ARM, and is designed specifically for multimedia system. It has high performance of video encoding and decoding. Therefore, in this paper, system based on DM6446 is designed to monitor and diagnose plant disease. The Overall Structure of The System The system mainly consists of plant video acquisition module, video transmission module and PC monitoring module, as shown in Fig. 1. TMS320DM6446 Video Acquisition & Compression Video Package Network PC Disease Diagnosis Video Playback Camera TVP5146 Y[2:9] VI_1_C VS VI_2_C DS PCLK SCL SDA SN74AVCB16245VR DM6446 Y[0:7] VD HD PCLK SCL SDA Fig. 1. Block Diagram of the System Fig. 2. Interface Circuit Diagram of TVP5146 and DM6446 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of Trans Tech Publications, (ID: , Pennsylvania State University, University Park, USA-05/03/16,03:40:24)

2 Applied Mechanics and Materials Vols The camera placed in the field captures the analog video signals of plant, which will be converted into digital signals in accordance with the ITU-R BT.656 standard and sent to DM6446. Then, DM6446 codes, compresses and packages the video, and then transfers it to the network. At last, PC monitoring center receives the compressed video from the network and displays it, also gets images and the relevant characteristics about the disease, and finally analyzes and diagnoses the plant disease by using a combination of neural network and expert system. Acquisition and Transmission of Plant Disease Video DM6446 is a hardware platform with dual-core architecture, system embeds UBoot, Linux kernel and file system in the ARM end, and the Linux operating system can run in DM6446 after the debugging work. Due to the embedded Linux operating system can easily realize the hardware control and multi-threaded processing [4], the system application can realize all functions including video collection, video coding, video stream package and package sending at the same time. The Acquisition of Plant disease video. The system utilizes the CCD camera to collect the video signal of plant. Analog video signal is input to the TVP5146 for decoding, and then the digital signal is output to SN74AVCB164245VR. SN74AVCB164245VR is a level conversion device of tri-state [5], it can achieve the conversion from 3.3 V (the logic level of TVP5146's output) to 1.8 V (the logic level of the DM6446's video input). By default, after the logic level conversion, SN74AVCB164245VR sends TVP5146's output directly to the video input interface of DM6446. DM6446 sends the video signal into the DSP subsystem for compression coding through the VPFE module. The interface circuit diagram between TVP5146 and DM6446 is shown in Fig. 2. The compression and transmission of plant disease video. This system adopts H.264 encoding algorithm to encode the plant video acquired, generating H.264 stream. The programs run in the embedded system of ARM, use and control the H.264 encoder of DSP to compress the video by calling the API function of VISA provided by Codec Engine [6,7]. DSP and ARM communicate and interact through the protocols called DSP / BIOS LINK on the physical link. H.264 encoding algorithm is very suitable for being transferred by streaming media technology due to the hierarchical design of VCL and NAL, so the system uses the RTP protocol to pack and transfer the H.264 video stream working as the streaming media server. At the same time, PC monitoring center uses the video player VLC as the receiving end to complete the reception and playback of H.264 video stream, constituting a complete video transmission system for H.264 video, as shown in Fig. 3. DM6446 H.264 video stream PC Play video Separate NALU Set the RTP header Send RTP Combine NALU Resolve RTP Receive RTP Network Fig. 3. Transmission System of H.264 Video Stream Fig. 4. Playback of Plant Disease Video The PC Monitoring and Diagnosis System of Plant Disease The acquisition of information on Plant disease. If it is found that the plant has diseases, insect pests or other anomalies during the video monitoring, we can use the screen-shot function of VLC to intercept the picture containing important information of disease, as shown in Fig. 4.

3 894 Mechatronics, Robotics and Automation We can get the information that the position of disease is on fruit and there is no mildew on surface by analyzing the video. Make further testing and analysis for the diseases image using Matlab to identify the basic characteristics of disease quickly and effectively. (1) Analyze the colour features of disease spot through its colour histogram. The values in the colour histogram of disease spot are from statistics, and they can reflect the colour distribution of disease spot, namely the occurrence probability of colour. Accordingly, we can select several colours with high occurrence probability as the main colours for the disease spot. The processing result is shown in Fig. 5. (2) Get the texture features of the disease spot. Edge and texture can delineate the disease spot's contour, can also pass most information of disease spot. The most common used edge detection operators including differential operator, Laplacian operator and Canny operator. Canny algorithm has a large signal-to-noise ratio, a high precision and a small amount of calculation, so system uses it to get the texture features of the disease spot. The processing result is shown in Fig. 6. Fig. 5. Distribution of Spot Colors Fig. 6. Texture Feature of Disease Spot Through the processing above, we can see that the shape of diseased part looks like moiré, and its main color is grayish green. The disease symptoms we get are used as a basis to judge in the diagnosis of the disease name. The diagnosis of plant diseases.taking the 13 common kinds of tomato diseases as the research object, system uses the LabVIEW combined with MATLAB to build the diagnosis module of plant disease. The neural network model of disease diagnosis is shown in Fig. 7. Since the neural network is a numerical calculation process, requiring the input and output data are numeric vectors, so the neural network should go through a conversion process including logic-to-digital (S/D) and digital-to-logic (D/S) before the communication with the outside world, i.e. we should express the symptoms and the names of diseases as several diagnostic parameters and encode them by binary. The Input of Tomato Disease Symptoms The Output of Tomato Disease Name S/D Conversion Neural Network of Disease Diagnosis D/S Conversion Parameters of Neural Network Fig. 7. Neural Network Model of Tomato Disease Diagnosis After encoding the diagnostic parameters and disease names, the system combines the coding of disease characteristics including the possible location of disease spot, the colours and shapes of disease spot, the colours and shapes of mildew layer and other major symptoms as a input vector of the neural network. The system also sets the coding corresponding to the disease name as the output of the network. Table 1 gives part output value of the neural network and its corresponding disease name.

4 Applied Mechanics and Materials Vols Table 1 Part Output Value of The Network Output values Name of disease 0000 damping-off 0001 Sheath Blight 0010 Early blight 0011 Late blight 0100 Botrytis The hidden layer and output layer of the neural network both adopt s-shaped activation function, and the network uses error sum of square i.e. e 2 as the error function. Since BP algorithm has the defects of slow convergence and long training time, so the system uses the improved BP algorithm with additional momentum method. Comparing the convergence effect of BP network, it is found that the effect is best when the momentum factor is set at 0.9. After determining the structure and learning algorithm of the network, do a lot of sample training for it. The changing curve of error during the training is shown in Fig. 8. We can see, when the training time approaches 1422, the error value of the network is reaching the expected error i.e which is set before. Fig. 8. Error performance curve of network Fig. 9. Interface of Disease Diagnosis After the training, in order to verify the correctness of the model, do network tests on it. Input 15 groups of test sample to the BP neural network, and it is found that the diagnostic accuracy rate is 100%. But it has differences in values between the actual output and the expected one. The accurate rate of training is 97.57% calculated by using Eq. 1. P = 1 O T ON N 100 Where: O ij the actual output values; ON the number of output unit; ( ( ) ( )) % ij ij T ij the expected output values; N the total number of samples. Thus, the error of the model is very small, and the diagnostic result is almost entirely consistent with the actual situation. All results prove the feasibility of BP neural network built in this system for diagnosis of tomato diseases. The Interface of disease diagnosis module is shown in Fig. 9. According to the symptoms of diseases which we get from processing the image of plant disease, choose the corresponding parts in the interface, and then click on the "Diagnose" button, the system will output the disease name, provide the corresponding picture, disease symptoms and pathogenesis, and give the method of prevention and treatment. The disease diagnosis module can timely and accurately diagnose the disease which the plant is suffering from, provide the occurrence regularity and control method of the disease, so as to control the disease situation effectively. (1)

5 896 Mechatronics, Robotics and Automation Conclusions This monitoring and controlling system for plant disease based on DM6446, have functions including video collection, video compression, video transmission, disease diagnosis and so on. This system is feasible in plant video surveillance and disease diagnosis. It is a successful application of information technology in the field of agriculture. Acknowledgements This work was financially supported by the National Natural Science Foundation ( ). References [1] Minghong Wang, Zhanhong Ma, Xiaohua Jin, Qian Liu, Shaojiong Sun: ACTA PHYTOPATHOLOGICA SINICA, Vol. 35, No. 6, pp , 2005 (In Chinese). [2] Juhua Luo, Wenjiang Huang, Chaoling Wei, Jihua Wang, Yuchun Pan: Transactions of the Chinese Society of Agricultural Engineering, Vol. 24, No. 12, pp , Dec (In Chinese). [3] Minghua Cheng, Jianping Chen: Journal of Shanxi Agricultural Sciences, Vol. 34, No. 2, pp , 2006(In Chinese). [4] Yanyan Wang, Chen Zhi, Junye Zhang: Computer Engineering and Design, Vol. 30, No. 4, pp , 2009(In Chinese). [5] Zhijun Li, Wanli Xu, Yulan Huang: Journal of Jilin University, Vol. 28, No. 6, pp , 2010(In Chinese). [6] Jinling Yang, Ying Chai, Hongwei Di: Electronic Measurement Technology, Vol. 33, No. 3, pp , 2010(In Chinese). [7] Zhe Zheng, Lei Zhao, Xiao Yu, Fei Han: Journal of Shandong University of Technology, Vol. 25, No. 5, pp , 2011(In Chinese).

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