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,实现自动精准的识别农作物病害以及病害程度的测定是对农作物病害防治的关键。【方法】本文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26种病害叶片的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个类别的总识别精度达到0.83左右,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,农作物病害的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。 |
关键词: 卷积神经网络 农作物病害 图像识别 |
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基金项目:非基金资助 |
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Crop Disease Identification Based on Convolutional Neural Network |
LiJianhua1, Hao Xin2, Niu Minglei3, Wang Junwei4, Li Pingan5, Yang Liguo6
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1.Zhaoxia Street Office, Baodi District,;2.Tianjin Agricultural Reclamation Bohai Agricultural Group Co., Ltd;3.Construction Service Center,Ministry of Agriculture and Rural Areas;4.Beijing Plant Protection Station;5.Agricultural Bureau of Taojiang County, Yiyang City, Hunan Province;6.Plant Protection and Plant Inspection Station of Inner Mongolia Autonomous Region
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Abstract: |
[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 the key to the prevention and control of crop diseases. [Method] This paper designs a crop disease identification method based on convolutional neural network and establishes a crop disease identification model. The model was trained 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. |
Key words: Convolutional neural network Crop diseases Image Identification |