Early Detection of Tomato Plant Disease Based on CNN Approach.
Year: 2025
Type: article
Abstract: Tomato plants are extremely vulnerable for a number of illnesses, hence we need to focus on their quality and productivity. Early and accurate detection of these diseases is essential to mitigate their effects and enhance crop management. Conventional disease identification methods often rely on expert visual inspection, which can be subjective, time-consuming, and prone to errors. With the advent of deep learning techniques, particularly convolutional neural networks (CNNs), automated disease detection has become a promising approach for precise and efficient agricultural diagnostics. This study focuses on leveraging CNN-based models to identify and classify tomato plant diseases from leaf images, addressing the need for a scalable, accurate, and user-friendly solution for disease detection.
Authors: Savitha A C, Madhu Kumar KM, Srushti S, Sujan S, Yashawini
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7091399887
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