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

文献类型： 外文期刊

第一作者： Zhao, Gang

作者： Zhao, Gang;Feng, Hao;Yu, Qiang;Tian, Qi;Yao, Linjia;Zhao, Gang;Yan, Changqing;Qu, Junjie;Yin, Ling;Feng, Hao;Yao, Ning

作者机构：

关键词： crop diseases; agricultural application; deep learning; disease identification

期刊名称：PEST MANAGEMENT SCIENCE （ 影响因子：3.8； 五年影响因子：4.3 ）

ISSN： 1526-498X

年卷期： 2024 年

页码：

收录情况： SCI

摘要： 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 x 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. RESULT:Our results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61-98.76%), Mixed (90.76-92.31%), and Field (68.56-73.81%). Although CSMs were found to reduce inter-crop disease misclassifications, they also led to a slight increase in intra-crop misclassifications. CONCLUSION: Our findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field-specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. (c) 2024 Society of Chemical Industry.

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