[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,...[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,and the aerial data map was converted into geotif image with longitude and latitude and then inputted into the detection system for preprocessing,mainly for image feature extraction and classification.Through deep learning of MATLAB software and BP neural network algorithm,the feature similarity of the images in the established characteristic database of cotton diseases and insect pests was compared.[Results]Through comparative analysis of characteristics of a large number of diseases and insect pests,it was found that deep learning method had high discrimination accuracy and good reliability.[Conclusions]The dynamic detection system using deep learning can well find cotton diseases and insect pests,and achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.展开更多
基金Supported by Natural Science Foundation of Xinjiang Uygur Autonomous Region(2020D01C003)。
文摘[Objectives]The paper was to find the diseases and insect pests in the process of cotton growth quickly,effectively and timely.[Methods]The growth process of cotton was dynamically monitored by UAV aerial photography,and the aerial data map was converted into geotif image with longitude and latitude and then inputted into the detection system for preprocessing,mainly for image feature extraction and classification.Through deep learning of MATLAB software and BP neural network algorithm,the feature similarity of the images in the established characteristic database of cotton diseases and insect pests was compared.[Results]Through comparative analysis of characteristics of a large number of diseases and insect pests,it was found that deep learning method had high discrimination accuracy and good reliability.[Conclusions]The dynamic detection system using deep learning can well find cotton diseases and insect pests,and achieve early detection and early treatment,so as to effectively improve the yield and quality of cotton.
