Detection of Banana Fusarium Wilt & Black Sigatoka: A Deep Learning Approach for Smallholder Farms in Central Uganda

Abstract

Bananas are a vital food and income source in Central Uganda, yet their cultivation is severely threatened by destructive diseases such as Fusarium Wilt and Black Sigatoka. Smallholder farmers, who form the backbone of Uganda’s agricultural sector, rely heavily on manual disease identification methods, which are time-consuming, error-prone, and largely ineffective for early intervention. This thesis proposes a hybrid deep learning approach that integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Gray Level Co-occurrence Matrix (GLCM) texture features to provide accurate, efficient, and scalable detection of banana leaf diseases using image-based classification. A dataset of over 17,000 annotated banana leaf images was sourced from the Lacuna Banana project. Rigorous preprocessing, including resizing, normalisation, and augmentation, was applied to enhance model robustness. Texture features extracted through GLCM were combined with spatial features learned by CNN and ViT models to improve classification sensitivity. Several models were developed and evaluated, including a custom CNN, InceptionV3 with transfer learning, and a ViT-based architecture. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess model performance. The Vision Transformer outperformed other individual models with 99% classification accuracy, while the proposed hybrid model achieved a balanced accuracy of 98%, with substantial precision and recall across all disease categories. The integration of GLCM features significantly improved the detection of texture-specific diseases like Black Sigatoka. This research contributes a robust, interpretable, and field-deployable AI-based diagnostic tool that aligns with Uganda’s national goals for data-driven agricultural development and the food security-related Sustainable Development Goals (and sustainable farming.