Research On Field Identification Technology Of Rice Planthopper Based On Machine Vision

Aimed at the problems of excessive spraying pesticide in paddy, we studied on the field identification technology of rice planthopper with machine vision. In order to obtain images of rice planthopper, we designed an equipment which was controlled by a remote computer. After image acquisition, the first operation was image preprocessing. The shape features of rice planthoppers were extracted by Hu moment, Improved Hu moment, Zernike moment and Krawtchouk moment. Then, BP Neural Network was used to train and test in order to know which the best one of the four moments was. The experiment was used Matlab2008to verify algorithm. It trained and tested on300samples of sogatella furcifera, laodelphax and nilaparvata lugens. The result showed that the correct rate of the recognition was the highest one which features were extracted by Krawtchouk moment, but the error recognition ratio of sogatella furcifera and laodelphax was high. Aimed at this problem, gray level co-occurrence matrix was used to extract the value of the back texture feature to discriminate of the three rice planthoppers. On this basis, the paper further used genetic algorithm to optimize BP neural network and particle swarm optimize BP neural network. By contrast, genetic algorithm and particle swarm optimization algorithm have their own advantages and disadvantages. Experimental results showed that the recognition result using genetic algorithm to optimize particle swarm algorithm has a faster solution. It can satisfy real time.The main contents and results of the research:(1) The determination of research projectThis paper studied real-time identification for rice planthopper in the field. Image acquisition used a mobile vehicle. It shot300samples of Sogatella furcifera, Nilaparvata lugens and Laodelphax. It shot the back shape of rice planthoppers. They were sent back to the remote PC by wireless way and identified by software.(2) Mobile equipment design In order to obtain images of rice planthopper, I designed an equipment which was controlled by a remote computer. The image size was set to640*480pixels because of the wireless transmission. The vehicle had simple structure and low cost. The camera was less than600yuan. These laid the foundation for low cost recognition system of rice planthopper.(3) Image preprocessingThe first operation was image preprocessing. Qality of image was not good in this condition of real-time system. It can not identify rice planthoppers by color. The best way was the shape. The commonly formula was used to get gray image, and OTSU algorithm was used to get binary image. In order to get better qualities of binary image, morphologic processing, Gauss filtering and median filtering were used.(4) Image feature extraction base on shapeThe shape features of rice planthoppers were extracted by Hu moment, Improved Hu moment, Zernike moment and Krawtchouk moment. After analysis and comparison, Krawtchouk moment not only reflected the global feature, but also exhibited better local feature. It was the highest correct recognition rate, but the error recognition ratio of sogatella furcifera and laodelphax was high.(5) Image feature extraction base on textureThe center of gravity was found and used as the center to construct gray level co-occurrence matrix. It used multiple annular routes. The texture features of Energy, entropy, moment of inertia and the related of gray level co-occurrence matrix was extract. Then used neural network to train and recognize. The identification rate of sogatella reached80%, and the rate of laodelphax reached90%, and the rate of nilaparvata lugens reached95%. It created the condition combining the features extractted by moment invariant.(6) Image classification and recognitionGenetic algorithm and particle swarm algorithm were used to optimize BP neural network. By contrast, genetic algorithm and particle swarm optimization algorithm have their own advantages and disadvantages. After analysis and comparison, GAIPSO Algorithm optimized BP Neural Network was better than genetic algorithm and IPSO Algorithm optimized BP Neural Network algorithm. It can realize satisfy real time.(7) Design the recognition system of rice planthopperThe software was designed through the above algorithms. The final goal was to achieve recognition and counting of rice planthoppers.(8) Field experimentThe system was tested in rice experiment stand of Nanjing Agricultural University. The curtain was placed on the observation road between the two fields, and the car was placed in front of the curtain. The car run to the best shooting position by remote control, and then used industrial camera to take pictures. The wireless network card transferred the images to the remote computer to further process. Field experiments showed that the system can work normally, and it can realize real-time recognition.