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

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 < 0.01), but fewer than 40% of respondents had reliable internet access. To bridge these gaps, we propose context-specific strategies: mobile-first design, localized training programs, and partnerships with agricultural extension services. By integrating technical and socio-economic insights, this study provides a roadmap for deploying computer vision in resource-constrained settings. Our rexsults underscore that while AI can transform disease management, success depends on tailoring solutions to the needs and constraints of Gambian smallholders.