Authors : Lawal, R.; Anjorin, T. S.; Aderolu, A. I.

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/z3znb53d

Scribd : https://tinyurl.com/3pnjnnrw

DOI : https://doi.org/10.38124/ijisrt/26jul1472

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Abstract : Smallholder tomato farmers in Northern Nigeria correctly identify major fungal diseases only about 41% of the time using unaided visual inspection, contributing to fungicide misapplication and avoidable yield loss. This study evaluated the field impact of SmartfarmerApp, a smartphone-based diagnostic application built on a validated convolutional neural network, on farmer disease identification accuracy. A pre-test/post-test controlled design allocated 240 tomato farmers across eight Local Government Areas in Kano and Kaduna States to an intervention group (n = 120, received the application) or a control group (n = 120, continued with conventional information sources), using computer-generated random allocation stratified by location and gender.

Keywords : Mobile Application, Field Trial, Diagnostic Accuracy, Smallholder Farmers, Difference-in-Difference, Tomato, Nigeria.

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