Year: 2025
Type: article
Abstract: Tomato cultivation is susceptible to various diseases, leading to significant yield loss and economic impact.Rapid and accurate prediction is essential for timely intervention and mitigation.Deep learning techniques, specifically Convolutional Neural Networks (CNN), are applied for automated detection of tomato leaf diseases.The methodology involves acquiring high-resolution images of tomato leaves and training a CNN model to classify them into healthy or diseased categories.The dataset comprises labeled images representing Early Blight, Late Blight, and healthy leaves.The CNN architecture is optimized to achieve high accuracy, precision, recall, and F1-score.The trained model demonstrates promising results in identifying tomato leaf diseases even under environmental variations and leaf deformities.The approach also allows for near real-time detection, enabling timely agricultural interventions.This research contributes to automated agricultural monitoring systems, aiding farmers in early disease detection and management, thereby enhancing crop productivity and sustainability.
Source: IJARCCE
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4413377485