Precision Hydration and Integrated Pesticide Management System for Paddy Fields using Machine Learning and IOT
Abstract
Background: Precision hydration optimizes water use in paddy fields via IoT sensors for soil moisture, while integrated pesticide management employs machine learning (ML) for real-time pest detection to minimize chemical inputs. This system addresses paddy crop challenges, such as water scarcity and pest infestations and enhances yield. The aim of this work is to project the results of machine learning models for paddy crop disease prediction that can analyze the plant health data sets using various algorithms. Methods: This starts with data collection and pre-processing, with missing information prevention and feature engineering. The system deploys a network of low-cost soil moisture, water level, temperature, humidity sensors and pest detection ML models across representative paddy fields. The ML models like CNN, YOLOv8, LSTM are used for plant disease prediction. Finally, efficient models are implemented in a user-friendly application whose reliability has been extensively tested. Results: Precision hydration optimizes water use in paddy fields via IoT sensors for soil moisture, while integrated pesticide management employs Machine Learning (ML) for real-time pest detection to minimize chemical inputs. The system addresses paddy crop challenges like water scarcity and pest infestations, enhancing yield. The developed system enables early detection of plant diseases and monitoring of the water supply to paddy fields, thereby improving the agricultural produce. Conclusion: This work envisions the development of an efficient technology for early detection of plant diseases, monitoring the water supply to paddy fields and thereby improving the agricultural produce. Generative AI is increasingly explored in precision agriculture for tasks such as synthetic training-data augmentation and natural-language farmer advisories; while such extensions are discussed as future work in this study, the present system's disease-prediction, irrigation, and pest-management outcomes are delivered entirely through the validated CNN, YOLOv8, and LSTM pipeline described here.
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