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Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning

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Abstract

Peanut leaf diseases that occur throughout the growth process of peanuts seriously affect the peanut yield and quality. The timely and accurate identification and diagnosis of disease with appropriate early treatment measures can effectively avoid the risk of yield and quality losses caused by leaf lesions. Due to the low professional knowledge level of plant growers, the traditional manual diagnosis exhibits low accuracy and causes manpower wastage. Therefore, the present study proposed an online recognition method for peanut leaf diseases based on the data balance algorithm and deep transfer learning. The data balance algorithm was used to solve the problem of data distribution tilt. Furthermore, transfer learning was used to construct a peanut leaf disease recognition model to enhance the generalisation ability based on the lightweight convolutional neural network by removing the original network output layer, re-adding the normalisation and pooling layers, modifying the fully connected layer, and introducing the regularisation constraint strategies. Finally, the deployment and analysis of the model were completed in low-cost embedded devices. This allowed the rapid on-site identification of the healthy state, black spot disease, brown spot disease, net spot disease and mosaic disease for single leaves. Using the self-built peanut leaf disease dataset, three lightweight convolutional neural networks, namely MobileNet V2, Xception and NasNetMobile, were trained and deployed. Comparative experiments showed that the average macro accuracy for peanut leaf disease recognition reached 0.978, 0.990 and 0.974. The average on-site diagnostic accuracy of peanut leaf diseases exceeded 85%. Thus, the present study provides new methods for the development of a portable peanut leaf disease diagnostic device.

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Acknowledgements

The authors would like to thank Zongfeng Zou and Julin Liu for providing valuable advice.

Funding

This work was supported by the National Natural Science Foundation of China under Grant No. 51775290 and the Key R&D Projects of Shandong Province under Grant No.2019GNC106140.

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Qingdao Agricultural University, 266109, Qingdao, China

    Qiang Feng, Pengfei Xu, Guangze Lan, Fangyan Wang, Dongwei Wang & Yuliang Yun

  2. College of Locomotive and Rolling stock Engineering, Dalian Jiaotong University, 116028, Dalian, China

    Qiang Feng

  3. Shandong Collaborative Innovation Center of Major Crop Mechanized Production Equipment, Qingdao Agricultural University, 266109, Qingdao, China

    Pengfei Xu, Guangze Lan, Fangyan Wang, Dongwei Wang & Yuliang Yun

  4. Smart Agriculture Research Institute, Qingdao Agricultural University, 266109, Qingdao, China

    Dexin Ma

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  1. Qiang Feng
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Correspondence to Dongwei Wang or Yuliang Yun.

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Feng, Q., Xu, P., Ma, D. et al. Online recognition of peanut leaf diseases based on the data balance algorithm and deep transfer learning. Precision Agric 24, 560–586 (2023). https://doi.org/10.1007/s11119-022-09959-3

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