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Plant Disease Identification and Pesticides Suggestion Using Deep Learning
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Author(s): B. Swapna (Dr. MGR Educational and Research Institute, India), G. Chaitanya Gowd (Dr. MGR Educational and Research Institute, India), G. Chiranjeevi (Dr. MGR Educational and Research Institute, India), S. Deepa (Velammal Engineering College, India), D. Senthil Kumar (RMK College of Engineering and Technology, India)and S. Anandhi (Dr. MGR Educational and Research Institute, India)
Copyright: 2026
Pages: 20
Source title:
Powering e-Collaboration Through AI, Machine Learning, and Internet of Things
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada)
DOI: 10.4018/979-8-3373-2372-5.ch008
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Abstract
Plant health management is vital for maximizing crop yields. However, traditional methods of identifying plant diseases and suggesting appropriate Pesticides are often labour-intensive, time-consuming, and prone to human error. It proposes an automated system that utilizes drones equipped with advanced imaging sensors and recommend suitable Pesticides based on real-time data. The drone captures high-resolution images of crops and analyses them using image processing techniques to identify symptoms of various plant diseases. By employing deep learning models trained on large datasets of diseased and healthy plant images, the system can classify the type and severity of the disease. Simultaneously, soil health data and environmental conditions are considered to suggest an optimal fertilizer plan for the affected area. This system provides several benefits, including faster disease detection, precise identification, reduced labor costs, and increased efficiency in Pesticides usage. It enables farmers to take timely and accurate actions, resulting in improved crop health and productivity
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