棉花病虫害特征检测方法分析研究

Analysis and study on characteristics and detection methods of cotton diseases and pests

为了快速、有效、及时发现棉花生长过程中出现的病虫害,采用无人机航拍动态监测棉花生长过程,将航拍数据图转化为带经纬度的geotif图像,输入检测系统进行预处理,主要进行图像特征提取与分类。通过Matlab软件进行深度学习,利用BP神经网络算法对已建立的棉花病虫害特征库里的图片进行特征相似度对比。经过大量病虫害特征对比分析发现,采用深度学习运算方法判别准确率较高,可靠性好。研究结果表明,运用深度学习的动态检测系统能够较好地发现棉花病虫害,做到早发现早治疗,从而有效提高棉花的产量和质量。

In order to quickly,effectively and timely find the diseases and pests in the process of cotton growth,UAV aerial photography is used to dynamically monitor the cotton growth process,convert the aerial data map into geotif image with longitude and latitude,and input the detection system for preprocessing,mainly for image feature extraction and classification,through the deep learning of Matlab software and BP neural network algorithm,the feature similarity of the pictures in the established cotton pest and disease feature database is compared.Through the comparative analysis of a large number of pest and disease features,it is found that the discrimination accuracy is high and the reliability is good.The results show that the dynamic detection system using deep learning can well find cotton diseases and pests,and can achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.