摘要
结合图像处理技术和机器学习算法,对水稻的3种最常见病害(即稻瘟病、白叶枯病和细菌性条斑病)进行识别和分类。首先,分割出水稻病害图像中的病斑部分并建立图像集,然后针对病理外在表现提取和优化病斑特征。接着,建立BP神经网络模型来根据优化后的特征来识别不同种类的水稻病害。最后,利用模拟退火算法结合自适应遗传算法,为BP算法选择合适的初始参数,以寻求最优解,改进分类模型。结果表明,改进后的NAGSA-BP算法具有较高的水稻病害识别准确率,具有可行性,且与传统的人工检测方法相比更加准确和高效。
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.
作者
陈悦宁
郭士增
张佳岩
蒲一鸣
Chen Yuening;Guo Shizeng;Zhang Jiayan;Pu Yiming(School of Electronic and Information Engineering,Harbin Institute of Technology,Harbin 150001,China)
出处
《电子技术应用》
2020年第9期85-87,93,共4页
Application of Electronic Technique
关键词
水稻病害识别
BP神经网络
自适应遗传算法
模拟退火算法
图像处理
identification of rice disease
BP neural network
adaptive genetic algorithm
simulated annealing algorithm
image processing
