Boosting Agricultural Diagnostics: Cassava Disease Detection with Transfer Learning and Explainable AI

Advances in artificial intelligence are revolutionizing agricultural diagnostics, particularly in addressing the critical challenge of cassava disease detection. Cassava, a vital food source for millions worldwide, faces significant yield losses due to various diseases that threaten food security in developing regions. This research presents a novel approach integrating transfer learning with explainable AI to create a robust disease detection system. Through extensive experimentation with multiple deep learning architectures, our ResNet-based model achieves a remarkable accuracy of 92% in distinguishing among four major cassava diseases and healthy specimens. The integration of SHAP (SHapley Additive exPlanations) technology provides unprecedented transparency in the model’s decision-making process, allowing stakeholders to understand how the neural network identifies disease-specific features. Our system demonstrates particular strength in identifying Cassava Mosaic Disease, achieving 98% accuracy, while maintaining robust performance across bacterial blight, brown spot and green mite detection. The methodology presented here not only advances the technical frontier of agricultural AI but also provides a practical tool for enhancing food security through early disease detection. This research establishes a foundation for developing accessible and interpretable AI systems that can be deployed in resource-limited agricultural settings, potentially transforming how farmers manage crop health in the digital age.