Early Detection of Tomato Leaf Diseases Using a Deep Learning Approach. A Field-Based Implementation in Ghana

Abstract

Tomato farming plays a major role in Ghana’s agricultural sector by contributing to food supply and serving as a source of income for many smallholder farmers. However, tomato plants are easily affected by leaf diseases that can spread quickly and reduce crop yield if not detected early. In many farming communities, disease detection is still done manually, which is often slow, inconsistent, and affected by human error. This study developed a deep learning-based system for the early detection of tomato leaf diseases using a Convolutional Neural Network (CNN) based on the VGG16 architecture. A total of 16,211 tomato leaf images, comprising healthy leaves and nine disease classes, were used for the study. The images were resized, normalized, and enhanced through data augmentation techniques such as rotation, flipping, shifting, and zooming. The model achieved its highest validation accuracy of 96.8% at epoch 16, while the final validation accuracy at epoch 20 was 94.30%, demonstrating strong performance in tomato disease classification.

Plant phenotyping relevance

トマト葉の病徴を画像からCNNで分類する手法を開発・評価しており、植物の病害状態を直接推定する方法が研究の中心である。

Code and data availability

The paper's mixed tomato leaf image dataset (16,211 images, ~60% field-collected in Ghana, 40% PlantVillage) and platform configuration settings are stated to be available only on request from the corresponding author. No public code, model checkpoints, or dataset deposit is provided; the Kaggle PlantVillage dataset is

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