Machine Learning-Integrated IoT System for Agricultural Monitoring and Control
Keywords:
Agricultural Land, High-Tech Sensors, Weather Forecast Prediction, Machine Learning Algorithms, Microcontrollers, Yield Prediction Problems
Abstract
In India, over 60.43% of land is used for agriculture, yet traditional farming methods struggle to meet the nation’s growing demands. This project proposes an economical, IoT-driven smart system to sustainably and efficiently utilize agricultural land. Controlled via a mobile app, the system integrates high-tech sensors and machine learning algorithms to optimize farming practices. The system employs electromagnetic, NPK, optical, and electrochemical sensors to analyze soil nutritional content and texture. Using advanced algorithms such as K-Means Clustering, Random Forest, or Decision Trees, it predicts the most suitable crops for cultivation. Infrared and laser sensors design optimal sowing patterns, maximizing yield. Soil moisture is continuously monitored to curate efficient irrigation methods, including drip irrigation. Integrated weather prediction forecasts precipitation and adjusts irrigation cycles to prevent over- or under-irrigation. During the crop growth phase, the module provides real-time updates on crop needs, alerting farmers through the mobile app. A versatile infrared sensor and alarm system enhance security by detecting motion and deterring predators. Governed by machine learning algorithms and powered by microcontrollers and Raspberry Pi, the system offers precise, data-driven solutions for modern agriculture. This smart approach aims to transform farming into a sustainable and productive enterprise.
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References
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Published
2024-11-28
How to Cite
Monisha, K., Jagatheeshwari, S., & Vaidianathan, B. (2024). Machine Learning-Integrated IoT System for Agricultural Monitoring and Control. Central Asian Journal of Theoretical and Applied Science, 5(7), 641-653. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1518
Section
Articles
