AI-Driven Mobile Crop Disease Detection for Smallholder Farmers: A Literature Review and Implications for Zimbabwe
Domain:
agricultureRecord type:
Crop diseases pose a significant threat to agricultural productivity, particularly for smallholder farmers in sub-Saharan Africa. Traditional methods of disease detection are often inadequate due to limited access to resources and expertise. Recent advancements in artificial intelligence (AI) and mobile technology offer promising solutions for early and accurate disease diagnosis. This literature review examines the application of AI-driven mobile tools in crop disease detection, with a focus on their relevance to smallholder farmers in Zimbabwe. The review synthesizes global and regional case studies, highlighting the strengths and limitations of existing technologies. Key findings indicate that while AI models, such as convolutional neural networks (CNNs), have demonstrated high accuracy in controlled settings, challenges remain in deploying these models in low-resource environments.