Year: 2025
Type: article
Abstract: Using a 2D Convolutional Neural Network (CNN) that uses prediction and classification techniques, we describe a novel method for automated tomato leaf disease detection in this work. Our dataset includes a wide range of classes, from healthy leaves to leaves with common diseases as bacterial spot, target spot, tomato leaf curl virus, blight, and septoria leaf spot. We carefully choose the dataset using methodical pre-processing and augmentation procedures to enable efficient training, validation, and testing. The CNN design combines convolutional and pooling layers with fully linked layers for accurate classification, effectively extracting significant characteristics from leaf images. By training the algorithm on this improved dataset, we are able to classify and predict tomato leaf diseases with amazing accuracy.
Author: A. Thamaraiselvi
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410540992