Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16527
Title: Ocimum Sanctum Linn Plant Fertilizer and Insecticide Spraying System with Disease Prediction Using Machine Learning
Authors: Aagaash, K R
Ramalakshmi, K
Venkatesan, R
Sundar, G Naveen
Nancy, Golden
Shirly, S
Keywords: Convolution Neural Networks (Cnn)
Fertilizer Spraying
Image Processing
Pesticide Spraying
Tulasi Leaf Disease Prediction
Issue Date: 2024
Publisher: Proceedings of the 3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024
Institute of Electrical and Electronics Engineers Inc.
Citation: pp. 743-746
Abstract: An integrated system for spraying pesticides and fertilizers on plants, coupled with machine learning capabilities for disease prediction, revolutionizes crop management, transforming crop management. This innovative solution administers precise doses of nutrients and pesticides to optimize crop health autonomously. Leveraging CNN algorithms and image processing, it predicts diseases proactively, mitigating risks. By integrating machine learning into traditional practices, it enhances efficiency and sustainability in terrace farming, tailoring care to each crop's needs. Continuous monitoring allows real-time adjustment of spraying, responding to changing conditions. The system learns from extensive plant image datasets to recognize disease patterns, empowering preemptive action. This proactive approach minimizes crop losses, promotes sustainability, and improves yields in terrace farming. © 2024 IEEE.
URI: https://doi.org/10.1109/ICAAIC60222.2024.10575800
https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16527
ISBN: 9798350375190
Appears in Collections:Conference Papers

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