Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop-specific approaches.

Journal: Pest management science
PMID: 39030887

Abstract

BACKGROUND: Crop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop-specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets.

Authors

  • Qi Tian
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, 310027 Hanghzou, China; Key Laboratory for Biomedical Engineering, Ministry of Education, China. Electronic address: Tianq@zju.edu.cn.
  • Gang Zhao
    Department of Vascular Surgery, The General Hospital of NingXia Medical University, Yinchuan, 750004, China.
  • Changqing Yan
    College of Intelligent Equipment, Shandong University of Science and Technology, Taian, China.
  • Linjia Yao
    College of Natural Resources and Environment, Northwest A&F University, Xianyang, China.
  • Junjie Qu
    Guangxi Crop Genetic Improvement and Biotechnology Key Lab, Guangxi Academy of Agricultural Sciences, Nanning, China.
  • Ling Yin
    National Population Health Data Center, Changping, China.
  • Hao Feng
    Value Pharmaceutical Services Co. Ltd, Nanjing, Jiangsu, China.
  • Ning Yao
    Department of Radiology, 66526Beijing Jishuitan Hospital, Beijing, PR China.
  • Qiang Yu
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. Electronic address: yuq@nwsuaf.edu.cn.

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