中圖分類(lèi)號(hào)： TN911.73；TP183 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.190998 中文引用格式： 陳悅寧,，郭士增,，張佳巖，等. 基于優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的水稻病害識(shí)別算法研究[J].電子技術(shù)應(yīng)用,，2020,，46(9)：85-87，93. 英文引用格式： Chen Yuening,，Guo Shizeng,，Zhang Jiayan，et al. Research on rice disease recognition algorithms based on optimized BP neural network[J]. Application of Electronic Technique,，2020,，46(9)：85-87，93.
Research on rice disease recognition algorithms based on optimized BP neural network
Chen Yuening,，Guo Shizeng,，Zhang Jiayan，Pu Yiming
School of Electronic and Information Engineering,，Harbin Institute of Technology,，Harbin 150001，China
Abstract： In this study, image processing technology and machine learning algorithm are combined to identify and classify the three most common diseases of rice, namely rice blast, bacterial leaf blight and bacterial streak. Firstly, the lesion part of rice disease image is segmented and the image set of rice disease is established. Then, according to the pathological appearance of different disease spots, characteristic parameters from various aspects are extracted and optimized. Then, BP neural network is used to build the model and classify the optimized features. Finally, the BP classification model is improved by optimizing the selection process of weights and thresholds in BP algorithm with simulated annealing algorithm and adaptive genetic algorithm. The results show that the improved algorithm has high accuracy in rice disease identification and is feasible. This method is more efficient and accurate than traditional manual diagnosis method.
Key words : identification of rice disease,；BP neural network,；adaptive genetic algorithm；simulated annealing algorithm,；image processing
