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<p>Ensuring food security is a crucial priority for nations worldwide, but plant diseases significantly hinder this objective, through their impacts on agricultural productivity. Chili pepper (<i>Capsicum spp.</i>) is a major crop in West Africa including in Benin. However, its production is challenged by diseases such as anthracnose and Tomato Yellow Leaf Curl Virus (TYLCV) which severely impact its yields and farmer livelihoods. While traditional methods for disease detection and management have been commonly used, they are no longer sufficient to combat rising pest infestations and declining agricultural productivity. To tackle this issue, we built a comprehensive dataset of 213 images of anthracnose-affected leaves, 202 images of TYLCV-infected leaves, and 119 images of healthy leaves, collected under diverse environmental conditions in Benin. The study compared the performance of thirteen (13) deep learning models including among others YOLOv8, MobileNetV2, and DenseNet121 for the classification of chili diseases in Benin using transfer learning techniques to improve their accuracy, based on values close to one (1) of evaluation metrics such as Accuracy, Precision, Recall and F1-Score. Results show that YOLOv8 outperforms other models in detecting and localizing leaf diseases in real time. It achieved a mean Average Precision (mAP@0.5) of 0.995 and mAP@0.5–0.95 of 0.941, demonstrating strong generalization. With precision and recall exceeding 99%, YOLOv8 proves highly reliable for agricultural applications. Among classification models, MobileNetV2 and DenseNet121 achieved 96.25% accuracy, confirming their effectiveness in disease classification. Our findings demonstrate that deep learning models, particularly YOLOv8, hold immense potential for real-time, automated detection of chili pepper diseases. Future research should focus on expanding datasets, integrating climatic variations, and improving disease severity assessment for enhanced agricultural sustainability.</p>

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Deep learning enables precision agriculture for sustainable chili pepper disease detection in Benin

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月7日

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  • Mireille Gloria Founmilayo Odounfa,
  • Castro Gbêmêmali Hounmenou,
  • Valère Kolawolé Salako,
  • Antoine Affokpon,
  • Romain L. Glèlè Kakaï

摘要

Ensuring food security is a crucial priority for nations worldwide, but plant diseases significantly hinder this objective, through their impacts on agricultural productivity. Chili pepper (Capsicum spp.) is a major crop in West Africa including in Benin. However, its production is challenged by diseases such as anthracnose and Tomato Yellow Leaf Curl Virus (TYLCV) which severely impact its yields and farmer livelihoods. While traditional methods for disease detection and management have been commonly used, they are no longer sufficient to combat rising pest infestations and declining agricultural productivity. To tackle this issue, we built a comprehensive dataset of 213 images of anthracnose-affected leaves, 202 images of TYLCV-infected leaves, and 119 images of healthy leaves, collected under diverse environmental conditions in Benin. The study compared the performance of thirteen (13) deep learning models including among others YOLOv8, MobileNetV2, and DenseNet121 for the classification of chili diseases in Benin using transfer learning techniques to improve their accuracy, based on values close to one (1) of evaluation metrics such as Accuracy, Precision, Recall and F1-Score. Results show that YOLOv8 outperforms other models in detecting and localizing leaf diseases in real time. It achieved a mean Average Precision (mAP@0.5) of 0.995 and mAP@0.5–0.95 of 0.941, demonstrating strong generalization. With precision and recall exceeding 99%, YOLOv8 proves highly reliable for agricultural applications. Among classification models, MobileNetV2 and DenseNet121 achieved 96.25% accuracy, confirming their effectiveness in disease classification. Our findings demonstrate that deep learning models, particularly YOLOv8, hold immense potential for real-time, automated detection of chili pepper diseases. Future research should focus on expanding datasets, integrating climatic variations, and improving disease severity assessment for enhanced agricultural sustainability.