International Journal of Science, Engineering and TechnologyAuthors: Amara Fuad Nyei, Dr. Vaibhav Bhushan Tyagi
Abstract: This paper presents CropGuard, a full-stack artificial intelligence-powered diagnostic and advisory system designed to bridge the critical crop disease knowledge gap confronting smallholder farmers in Liberia and Sub-Saharan Africa. Agriculture constitutes approximately 36 percent of Liberia’s gross domestic product, yet annual pathogen-induced yield losses of 30 to 80 percent and a national extension officer-to-farmer ratio of 1:35,000 leave the overwhelming majority of the country’s 338,492 farming households without timely diagnostic support. CropGuard integrates a MobileNetV3-Small deep learning backbone-trained on PlantVillage and Makerere University AI Lab datasets – to classify 16 disease categories across five core Liberian staple crops: Bean, Cassava, Corn, Potato, and Tomato. Google Gemini 3.0 Flash Preview synthesises diagnostic outputs into localised agronomic remediation advice across six languages, with Text-to-Speech delivery serving the 64.5 percent of rural female-headed households with no formal schooling. Technical evaluation confirms a validation accuracy of approximately 86 percent with inference latency below 200 milliseconds, demonstrating the system’s viability as a low-cost, scalable solution aligned with Liberia’s National Agricultural Development Plan (NADP 2024–2030) and the continent-wide imperative for digitally-enabled food sovereignty.
DOI: http://doi.org/10.5281/zenodo.20639226
