Year: 2025
Type: article
Abstract: Background: Agriculture is a vital sector, providing livelihood and food for a large population. Ensuring plant health is essential for sustainable crop production. Need and Methods: This work proposes a deep learning-based system using Convolutional Neural Networks (CNNs) for automatic detection of plant diseases from leaf images. The model identifies symptoms early, thus supporting timely treatment and reducing crop loss. Results: The system accurately detects plant diseases, recommends suitable fertilizers, and displays real-time sensor data such as temperature and humidity, all on a monitoring interface. Conclusion: Deep learning enables fast, accurate, and automated disease detection, improving crop health and agricultural productivity.
DOI: 10.55041/ijsrem44105
Authors: N. Manoj Kumar, N. Tejaswi, J. Anitha, Mohammad Inzamamul Huq, K.B. Srinidhi, Dr. G. Sateesh, Mrs. V. Anupama
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W4409307457