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Image recognition of rice leaf diseases using atrous convolutional neural network and improved transfer learning algorithm

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Abstract

Rice diseases pose a great threat to abundant yield, stable harvest, and high-quality production of rice in China. Among them, the four diseases causing the most significant yield loss are sheath blight, rice blast, false smut, and ear rot. Accelerating the diagnosis and accurate identification of rice diseases is very important for the future development of rice production. To enhance the diagnostic accuracy of traditional CNNs in small-sample rice disease image sets, this paper proposes an ACNN-TL model based on CNN structure combined with Atrous convolution and transfer learning. Compared with standard convolution, Atrous convolution can increase the size of the receptive field in feature extraction and enrich the extracted feature details. Transfer learning can use knowledge in the original model to enhance capability in the target task. The experimental results show that the accuracy of ResNet-34, VGG-16, and AlexNet combining Atrous convolution and Transfer learning is 98.4%, 97.9%, and 95.9%, which increased 8.7%, 9.4%, and 16.7%, respectively, compared to with the original model. It can be seen that the combination of Atrous convolution and Transfer learning can effectively improve the diagnostic accuracy of CNN for small sample rice diseases, and the best model is Atrous ResNet-34 with Transfer learning.

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Data will be made available on reasonable requests.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61873058 and 61933007, the Key Projects of Heilongjiang Natural Science Foundation under Grant ZD2019F001, Heilongjiang Natural Science Foundation under Grant LH2020F042, China Postdoctoral Science Foundation under Grant 2016M591560, Heilongjiang Postdoctoral Financial Assistance under Grant LBH-Z15185, the Scientific Research Starting Foundation for Post Doctor from Heilongjiang under Grant LBH-Q17134, Heilongjiang Bayi Agricultural University Innovative Research Team Foundation under Grant TDJH201807 and the Open Fund of the Key Laboratory for Metallurgical Equipment and Control of Ministry of Education in Wuhan University of Science and Technology under Grants 2018A02 and MECOF2019B02.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing, 163319, Heilongjiang, China

    Yang Lu, Xianpeng Tao, Feng Jiang & Jiaojiao Du

  2. Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Whhan, 430081, Hubei, China

    Gongfa Li

  3. the Department of Mathematics, Yangzhou University, Yangzhou, 225009, Jiangsu, China

    Yurong Liu

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  1. Yang Lu
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Correspondence to Yang Lu.

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Xianpeng Tao, Feng Jiang, Jiaojiao Du, Gongfa Li and Yurong Liu contributed equally to this work.

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Lu, Y., Tao, X., Jiang, F. et al. Image recognition of rice leaf diseases using atrous convolutional neural network and improved transfer learning algorithm. Multimed Tools Appl 83, 12799–12817 (2024). https://doi.org/10.1007/s11042-023-16047-9

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