基于AI的桃树病害智能识别方法研究与应用

Research and Application of Intelligent Recognition Method of Peach Tree Diseases Based on AI

为解决传统人工识别桃树病害效率低、成本高、准确率低等问题,提出了基于AI深度学习的桃树病害智能识别方法,利用并微调ImageNet预训练的DenseNet-169分类模型,对桃树常见的11种病害图像进行预处理与模型训练,搭建桃树病害智能识别软件环境。该方法对常见桃树病害的平均识别率达到91%以上,结合图像处理、深度学习、数据挖掘等技术自动对桃树病害进行识别,实现桃树病害的智能诊断并提供防治建议。该方法具有人力成本低、操作简单、识别效率高等优点,利于病害的及时诊出与防治决策的制定,对促进果园病害防控的智慧化管理具有重要研究意义与应用价值。

In order to solve the problems of low efficiency,high cost and low accuracy of traditional methods of manually identifying peach tree diseases,an intelligent recognition method of peach tree diseases based on AI deep learning was proposed.Using and fine-tuning the DenseNet-169 classification model pre-trained by ImageNet,data preprocessing and model training were performed on the image of 11 common diseases of peach trees,then the web terminal was built to integrate and develop a software system for intelligent recognition of peach tree diseases.The average recognition rate of these 11 peach tree diseases was over 91%by this method.Using image recognition technology to automatically identify peach tree diseases,combined with modern science and technology such as image processing,deep learning,data mining and analysis,the intelligent diagnosis and prevention suggestions for peach tree diseases were realized.This proposed method could reduce labor costs,simplify operations,and improve recognition efficiency,so it was conducive to timely diagnosis and decision-making for prevention and treatment of diseases.Therefore,this research had important significance and application value for promoting the intelligent management of orchard disease controling,and provided support for the research and practice of image recognition methods based on deep learning with small sample sets.