Precision diagnosis of tomato diseases for sustainable agriculture through deep learning approach with hybrid data augmentation
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Date
2025
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Abstract
Tomato is a key crop in global agriculture, yet it faces yield and quality challenges due to various diseases.
Traditional disease identification methods are slow and require expertise, limiting their practicality in large-scale
farming. Integrating automated disease detection with precision agriculture provides a timely, accurate diag
nosis, promoting sustainable practices. However, the scarcity of real-world data hampers effectiveness. To
address this issue, data augmentation techniques simulate variations in farm images, enriching datasets for
improved detection of diseases. This investigation aims to identify seven different tomato diseases, such as
bacterial spot, early blight, late blight, and others, while also detecting healthy plant leaves. Unlike previous
studies that relied on the controlled PlantVillage dataset, this study utilizes the real-world PlantDoc dataset. The
study addresses different challenges faced throughout the model development process, like data scarcity and
imbalances. A hybrid data augmentation technique is introduced to increase the dataset size from 737 images to
6696 images, which improves the accuracy and robustness of the computer vision model. The study employs the
YOLOv8n deep convolutional neural network, achieving 96.5 % mAP, 97 % precision, 93.8 % recall, and 95 % F1
score. The results demonstrate a significant improvement in disease detection, addressing challenges from
inadequate datasets and advancing AI-driven precision agriculture. The proposed YOLOv8n model has the po
tential to be applied beyond its current scope by training it on datasets of other crops. The model can learn and
generalize the unique image features associated with various crop types, expanding its utility in agricultural
applications. This flexibility allows the model to detect and classify plant characteristics, diseases, or pests across
different crops, enabling its use in diverse agricultural environments. As a result, the YOLOv8n model could serve
as a robust tool for precision farming, helping to optimize crop management and enhance productivity on a
broader scale.