基于AI的桃树病害智能识别方法研究与应用

doi:10.13304/j.nykjdb.2021.0935

摘要/Abstract

摘要：

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。

关键词: 桃树病害, 图像识别, 深度学习, DenseNet模型

Abstract:

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.

Key words: peach diseases, image recognition, deep learning, DenseNet model

中图分类号:

S126

吴建伟, 黄杰, 熊晓菲, 高晗, 秦向阳. 基于AI的桃树病害智能识别方法研究与应用[J]. 中国农业科技导报, 2022, 24(5): 111-118.

Jianwei WU, Jie HUANG, Xiaofei XIONG, Han GAO, Xiangyang QIN. Research and Application of Intelligent Recognition Method of Peach Tree Diseases Based on AI[J]. Journal of Agricultural Science and Technology, 2022, 24(5): 111-118.

图/表 7

图1 经过不同方法处理的桃黑星病图像

Fig.1 Images of peach black spot disease processed by different methods

图2 DenseNet改进模型结构

Fig2 DenseNet improved medel structure

图3 桃树病害智能图像识别系统

Fig.3 Intelligent image recognition system of peach diseases

图4 模型训练的损失值与准确率

Fig.4 Loss value and accuracy curve of training model

表1 桃病害图像识别DenseNet-169改进模型测试结果

Tab.1 Test results of DenseNet-169 improved model for peach tree diseases image recognition

桃树病害类型 Type of peach tree disease	样本数 Number of samples	准确率 Accuracy rate/%
桃黑斑病 Peach black spot	569	98.28
桃褐腐病 Peach brown rot	336	94.12
桃黑星病 Peach scab	280	92.86
桃炭疽病 Peach anthracnose	282	96.55
桃缩叶病 Peach leaf curl	268	92.31
桃灰霉病 Peach botrytis cinerea	235	86.96
桃褐斑穿孔病 Peach brown spot perforation	208	85.00
桃霉斑穿孔病 Peach mildew and perforation	290	86.21
桃细菌性穿孔病 Peach bacterial perforation	283	85.71
桃树流胶病 Peach gummosis	268	92.31
桃树木腐病 Peach wood rot	244	95.83
负样本 Negative sample	1 000	98.00
总计 Total	4 263	93.65

图5 DenseNet-169模型改进前后对比曲线

Fig.5 Comparison curve before and after improvement of DenseNet-169 model

图6 桃树病害智能图像识别系统界面

Fig.6 Interface of intelligent image recognition system for peach diseases

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