基于CNN的植物叶片病害智能检测系统算法设计与仿真

Design and Simulation of Plant Leaf Disease Intelligent Detection System Algorithm Based on CNN

针对目前基于机器学习的农作物叶片病害识别算法中训练收敛误差大、识别精度不高等问题,文章研究了基于卷积神经网络CNN的植物叶片病害智能检测系统算法。通过对采集到的叶片图像进行K-means聚类算法分割叶片图像中受感染的区域,再用CNN网络进行特征提取及识别分类,实现对农作物叶片病害的检测。仿真试验数据表明,以马铃薯植株叶片为例,设计的算法模型平均识别精度为94.7%,较SVM提高了6.15%,适用于植物叶片病害智能检测系统。

Aiming at the problems of large training convergence error and low recognition accuracy in current crop leaf disease recognition algorithms based on Machine Learning,this paper studied the intelligent detection system algorithm of plant leaf disease based on convolutional neural network CNN.Through the K-means clustering algorithm on the collected leaf images to segment the infected areas in the leaf images,and then use CNN network for feature extraction and recognition classification,to achieve the detection of crop leaf diseases.The simulation experimental data shows that,taking potato plant leaves as an example,the average recognition accuracy of the algorithm model designed in this paper is 94.7%,which is 6.15%higher than that of SVM.It is suitable for the intelligent detection system of plant leaf diseases.