基于卷积神经网络的魔芋病害识别  

Identification of konjac disease based on Convolutional Neural Network

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作  者:雷朦 余顺园 LEI Meng;YU Shun-yuan(College of Electronics and Information Engineering,Ankang University,Ankang 725000,Shaanxi,China)

机构地区:[1]安康学院电子与信息工程学院,陕西安康725000

出  处:《湖北农业科学》2022年第23期197-201,228,共6页Hubei Agricultural Sciences

基  金:国家自然科学基金项目(61801005);陕西省自然科学基础研究计划-青年项目(2020JQ-903);陕西省教育厅科研计划项目-自然科学一般专项(21JK0465)。

摘  要:魔芋在种植过程中易感染各种病害,为了对魔芋病害进行实时自动化监测,研究了基于机器视觉的魔芋病害自动识别算法。以Inception V3为卷积神经网络算法理论模型,在深度学习开发环境下,采用神经元结构算法,以神经元为基本单位组建神经网络,实现了魔芋病害种类的识别。通过归一化和细化等预处理提升病害识别的精度和准确度,对模型内部及结果进行可视化处理以增加算法的实用性;在识别过程中通过调节各参数及层结构对模型进行优化,使模型能够较好地兼顾准确率和效率。测试结果表明,该算法能够实现常见魔芋的自动病害识别,准确率保持在90%以上。Konjac is susceptible to various diseases during the planting process.In order to automatically monitor the konjac disease in real time,the automatic identification algorithm of konjac disease based on machine vision was studied.Taking Inception V3 as the theoretical model of Convolutional Neural Network(CNN)algorithm,under the deep learning development environment,using the neuron structure algorithm,the neural network was built with neurons as the basic unit,and the identification of konjac disease types was realized.The precision and accuracy of recognition were improved through preprocessing such as normalization and refinement,and the internal and results of the model were visualized to increase the practicability of the algorithm.In the process of recognition,the model was optimized by adjusting the parameters and layer structure,so that the model could better balance accuracy and efficiency.The test results showed that the proposed algorithm could realize automatic disease identification of common konjac,and the accuracy rate was kept above 90%.

关 键 词:魔芋病害 卷积神经网络 Inception V3 深度学习 

分 类 号:S24[农业科学—农业电气化与自动化]

 

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