摘要
【目的】农作物生长过程中,作物产量会受到各种病害影响,实现自动精准地识别农作物病害以及病害程度的测定是农作物病害防治的关键。【方法】文章设计了一种基于卷积神经网络的农作物病害的识别方法并建立了农作物病害识别模型,模型利用10种作物中常见的59种病害类型的叶片图像数据集进行训练,并对模型的训练过程和训练结果进行评估。【结果】(1)农作物病害识别模型对59种病害类型的总识别精度达到0.83,部分类别的识别率高于0.9;(2)当训练的迭代次数增加到50轮以上时,农作物病害识别模型的性能不再提升,此时数据集图像的数量对模型性能的影响较大。【结论】实验证明,利用卷积神经网络进行农作物病害识别具有较高的可行性和准确性,为农作物病害的防治打下基础。
[Purpose]During crop growth,yield is affected by various diseases.The realization of automatic and accurate identification of crop diseases and the determination of disease degree is key to the prevention and control of crop diseases.[Method]In this paper we design a crop disease identification method based on convolutional neural network and establishes a crop disease identification model.The model was trained by using image datasets of 24 diseased leaves in 10 crops,and the training process and training results of the model were evaluated.[Result]The identification model of crop diseases has a total recognition accuracy of about 0.83 for 59 categories,and the recognition rate of some categories is higher than 0.9.When the number of iterations of training increases to more than 50 rounds,the performance of the crop disease identification model is no longer improved.At this time,the number of dataset images has a greater impact on the model performance.[Conclusion]Experiments show that the use of convolutional neural networks for crop disease identification has high feasibility and accuracy,laying a foundation for the prevention and control of crop diseases.
作者
李建华
郝炘
牛明雷
王俊伟
李平安
杨立国
Li Jianhua;Hao Xin;Niu Minglei;Wang Junwei;Li Pingan;Yang Liguo(Zhaoxia Street Office,Baodi District,Tianjin 301800,China;Tianjin Agricultural Reclamation Bohai Agricultural Group Co.,Ltd,Tianjin 301823,China;Construction Service Center,Ministry of Agriculture and Rural Affairs,Beijing 100081,China;Beijing Plant Protection Station,Beijing 100029,China;Agricultural Bureau of Taojiang County,Yiyang 413499,China;Plant Protection and Plant Inspection Station of Inner Mongolia Autonomous Region,Hohhot 010010,China)
出处
《中国农业信息》
2019年第3期39-47,共9页
China Agricultural Informatics
关键词
卷积神经网络
农作物病害
图像识别
convolutional neural network
crop diseases
image identification
