Ai-powered offline maize streak virus (MSV) detection system for smallholder farmers: design, implementation, and evaluation
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Date
2026-06-05
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Uganda Christian University
Abstract
Maize Streak Virus (MSV) remains a highly destructive agricultural pathogen in Sub-Saharan Africa, severely undermining crop yields and threatening smallholder food security. While laboratory diagnostics like PCR and LAMP offer high sensitivity, they remain financially and logistically inaccessible to rural farmers who lack timely extension support. Deep learning and mobile computer vision present a transformative alternative for automated plant disease detection. However, most existing digital solutions assume persistent internet connectivity, cloud infrastructure, or premium hardware, rendering them impractical under the rigid network and power constraints of rural African fields. To bridge this technology adoption gap, this project presents MaizeGuard, an inclusive, dual platform, entirely offline-first AI-powered diagnostic ecosystem engineered for localized MSV classification. The system integrates an optimized MobileNetV2 convolutional neural network architecture trained via transfer learning to classify maize leaves in real time into three categories: Healthy Maize Leaf, MSV Infected Leaf, and Not a Maize Leaf. For smartphone users, the MaizeGuard mobile tier utilizes a React Native and Expo framework integrated with ONNX Runtime for on-device inference and Async Storage for local caching. For users lacking smartphone access, a standalone hardware kit was built using a Raspberry Pi microcomputer, a CSI camera, a 3.5-inch resistive TFT touchscreen, and a custom Python framebuffer application configured via systemd to auto-launch at boot. An optional Node.js/PostgreSQL backend supports asynchronous data synchronization without interfering with core offline diagnoses. The system engineering process was grounded in a systematic review of 24 peer-reviewed studies, which validated lightweight CNN optimization pathways for edge environments and highlighted the scarcity of farmer-ready MSV diagnostic systems. System evaluation encompassed functional validation, multi-platform integration benchmarking, execution latency tracking, thermal profiles, and touchscreen calibration durability under simulated field setups. Experimental results demonstrated that the completed dual-platform ecosystem achieves high classification accuracy, low computational latency, and robust operational resilience under low-resource field conditions. This work contributes a practical, scalable advancement in digital precision agriculture, demonstrating how edge AI design can deliver inclusive diagnostic tools directly to
smallholder farmers in Uganda.
Description
Undergraduate