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

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

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Google Scholar : https://tinyurl.com/2w2h6ffz

Scribd : https://tinyurl.com/3w7t3ww6

DOI : https://doi.org/10.38124/ijisrt/26jul1471

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Abstract : Accurate, field-deployable diagnostic tools are needed to close the diagnostic gap that limits fungicide targeting among smallholder tomato farmers in Northern Nigeria. This study developed and validated a lightweight convolutional neural network (CNN) for automated diagnosis of five major tomato fungal diseases plus three additional common conditions, trained on field-collected leaf images from Kano and Kaduna States. A MobileNetV2 architecture pre-trained on ImageNet was fine-tuned via transfer learning on more than 10,000 images across ten disease and health classes, using farm-level dataset splitting to prevent data leakage and five-fold cross-validation for model selection.

Keywords : Convolutional Neural Network, MobileNetV2, Transfer Learning, Plant Disease Detection, Tomato, Deep Learning, Nigeria.

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