Application of Computer Vision Models for Detecting and Classifying Crop Diseases in Gambian Farms

Abstract

Abstract Crop diseases threaten food security in The Gambia, where agriculture employs 70% of the population. This paper explores the application of computer vision models to help farmers detect and classify crop diseases more effectively, leveraging deep learning techniques—particularly convolutional neural networks (CNNs). Our fine-tuned VGG16 model achieved 91.6% accuracy in identifying diseases like rice blast and cassava mosaic, demonstrating the potential for scalable, low-cost diagnosis. The study evaluates custom CNNs, transfer learning (VGG16, ResNet50, MobileNetV2), and image preprocessing techniques (segmentation, augmentation) to optimize performance for Gambian farm conditions. Beyond technical validation, the paper highlights real-world adoption barriers, including limited technology access, farmer skepticism, and infrastructural gaps. Through surveys and interviews with 120 + farmers, we found that awareness of AI tools strongly correlates with willingness to adopt them (r = 0.63, p

Code and data availability

The paper describes a curated dataset of Gambian crop disease images and model training (custom CNN, VGG16, ResNet50, MobileNetV2), but contains no data or code availability statement, no public deposit, no author URL for assets, and no supplement. The PlantVillage dataset is a generic third-party repository, not a pap

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