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A Deep Learning based Phyto-pathological Detection for Early Diagnosis of Tomato Leaf Diseases
Abstract
Tomato (Lycopersicon esculentum Mill) is a frequently grown vegetable in various regions of the world. Tomatoes are one of the most demanded vegetables and a source of revenue for third-world countries. Tomato plant diseases can significantly reduce tomato production and impact a nation’s economic development. Timely and reliable detection of tomato leaf diseases can increase tomato production, ensure food security, and sustain agricultural activities. A Convolutional Neural Network (CNN) model was employed in this study to detect and classify Fungal, Bacterial, and Viral diseases in nine types of tomato leaves. High-resolution images of plant leaves damaged by pathogens were used for training and testing the CNN model. The dataset used in Experiment 1 consisted of nine different tomato leaf classes, including one healthy leaf class and eight pathogen-affected classes. The dataset used for Experiment 2 was categorized into three classes based on the pathogens: bacteria, fungi, and viruses. Experiment 1 achieved an accuracy of 93%, and Experiment 2 achieved 96% accuracy in detecting tomato leaf diseases. The results demonstrate that the deep learning-based Convolutional Neural Network (CNN) model is an advanced and effective solution for the identification of plant leaf diseases. This research will support better crop planning and help increase overall tomato production.
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References
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DOI: 10.33687/phytopath.014.02.5885
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