ItemConference Paper

Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models

Title
Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models
Creator
Punitha, A.; Jayamangala, S.; Joel Josephson, P.; Bharathi, K.; Sindhu, V.; Singh, Kamlesh
Description
Pests and diseases greatly reduce crop quality and yield; therefore, IA relies on effective pest and disease control. UAVs have become a crucial remote sensing (RS) tool for agricultural process monitoring and management. This study will examine major advances in this field using bibliometric methodologies including author co-occurrence and keyword co-contribution studies. The suggested technique involves preprocessing, feature extraction, and model training. Data quality improves with preprocessing. UAV images are used for feature extraction, focusing on canopy structure and height. PPO is trained the prediction model. Compared to ultramodern GANs and LSTM networks, the recommended model wins. The model consistently outperforms competitors with 91.17 percent accuracy. The study suggests employing UAVs in smart farming to reduce pests and diseases. The suggested model's accuracy and reliability improve crop quality and production by solving agricultural monitoring and management problems. 2025 IEEE.
Source
3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
crop diseases and pests; proximal policy optimization (PPO); remote sensing (RS)
Coverage
Punitha A., Chettinad College of Engineering and Technology, Department of Computer Science and Engineering, Karur, India; Jayamangala S., Santhiram Engineering College Nandyal, Department of Electronics and Communication Engineering, Nandyal, India; Joel Josephson P., Malla Reddy Engineering College, Department of Electronics and Communication Engineering, Dhulapally, Medchal, India; Bharathi K., Erode Sengunthar Engineering College, Department of Agricultural Engineering, Erode, India; Sindhu V., Christ University, Department of Computer Science, Bangalore, India; Singh K., School of Management Sciences, Department of Mechanical Engineering, Uttar Pradesh, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833150845-6;
Format
online
Language
English
Type
Conference paper

Citation

Punitha, A.; Jayamangala, S.; Joel Josephson, P.; Bharathi, K.; Sindhu, V.; Singh, Kamlesh, “Crop Disease and Pest Management in Agriculture via UAV Remote Sensing and Advanced Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26010.

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