Effect of a Smartphone-Based Diagnostic Application on Tomato Farmer Disease Identification Accuracy: A Controlled Field Evaluation in Northern Nigeria

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.

Plant phenotyping relevance

トマトの病害状態を画像・視覚観察から判定するスマートフォン診断アプリを対象に、現場での識別精度を対照評価しており、植物病害フェノタイピング手法の応用・検証が中心である。

Code and data availability

The article describes a smartphone diagnostic app (SmartfarmerApp) and field trial, but contains no data availability statement, no public dataset, no image/sensor inputs, and no author code or model deposit with a public URL. Only the article DOI is present.

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