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<p>Agriculture is essential to the economy and social well-being of any community. Early diagnosis of plant diseases is critical to crop protection and sustainable agriculture. Deep learning (DL) has demonstrated outstanding performance in disease detection, but its complexity has made field implementation difficult. To overcome this issue, lightweight DL models have been developed. This paper presents a comparative analysis of lightweight DL models for plant disease classification on three publicly available data sets: PlantDoc, Mango, and Soybean Leaf Disease. The PlantDoc dataset has 28 classes with different crops and disease conditions captured under real field conditions. The Mango data set has images of 7 disease classes and 1 healthy class, and the Soybean data set has 2 disease classes and 1 healthy class, with multiple leaves per frame, depicting real-world scenarios. This study compares lightweight classification models like ShuffleNetV2, MobileNetV2, and MobileNetV3Small and object detection models YOLOv8n and YOLOv11n, modified for classification via bounding box class filtering. The models were evaluated using conventional evaluation metrics such as accuracy, precision, recall, F1 score, and Cohen’s kappa score; additionally, model size and number of parameters are considered in comparison. Experiments demonstrate that MobileNetV2 achieves the highest accuracy of 89.86% on the PlantDoc dataset (augmented), and MobileNetV3Small achieves the highest accuracy on the Mango dataset (99.59% without augmentation) and achieves an accuracy of 96.49% on the Soybean dataset. Ablation studies were conducted by applying pruning and dynamic quantization on models. Models were also deployed on Internet of Things (IoT) devices, such as the Raspberry Pi 5, for performance evaluation. Further, this study states the current limitations present and suggests future directions for efficient deployment of DL models on edge devices for improved crop disease identification.</p>

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Evaluation of lightweight and efficient deep learning models for plant disease classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

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  • Sangeeta Duhan,
  • Preeti Gulia,
  • Nasib Singh Gill,
  • Teresa A. Oliveira,
  • Pradeep Kumar Singh

摘要

Agriculture is essential to the economy and social well-being of any community. Early diagnosis of plant diseases is critical to crop protection and sustainable agriculture. Deep learning (DL) has demonstrated outstanding performance in disease detection, but its complexity has made field implementation difficult. To overcome this issue, lightweight DL models have been developed. This paper presents a comparative analysis of lightweight DL models for plant disease classification on three publicly available data sets: PlantDoc, Mango, and Soybean Leaf Disease. The PlantDoc dataset has 28 classes with different crops and disease conditions captured under real field conditions. The Mango data set has images of 7 disease classes and 1 healthy class, and the Soybean data set has 2 disease classes and 1 healthy class, with multiple leaves per frame, depicting real-world scenarios. This study compares lightweight classification models like ShuffleNetV2, MobileNetV2, and MobileNetV3Small and object detection models YOLOv8n and YOLOv11n, modified for classification via bounding box class filtering. The models were evaluated using conventional evaluation metrics such as accuracy, precision, recall, F1 score, and Cohen’s kappa score; additionally, model size and number of parameters are considered in comparison. Experiments demonstrate that MobileNetV2 achieves the highest accuracy of 89.86% on the PlantDoc dataset (augmented), and MobileNetV3Small achieves the highest accuracy on the Mango dataset (99.59% without augmentation) and achieves an accuracy of 96.49% on the Soybean dataset. Ablation studies were conducted by applying pruning and dynamic quantization on models. Models were also deployed on Internet of Things (IoT) devices, such as the Raspberry Pi 5, for performance evaluation. Further, this study states the current limitations present and suggests future directions for efficient deployment of DL models on edge devices for improved crop disease identification.