IoT-Enabled Intelligent Irrigation System with Machine Learning-Based Monitoring, for Effective Rice Cultivation
Keywords:
IoT, machine learning, intelligent irrigation, rice cultivation, sustainable agricultureAbstract
The objective of this study is to examine how IoT-enabled intelligent irrigation systems can be used in rice agriculture. The study uses sensors to gather environmental data in real time, such as temperature, water level, humidity, and humidity sensors. Machine learning models, such as artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and random forests (RF), are then used to process the data in order to predict future water demand. Experimental findings The efficacy of the system indicates , where ANN demonstrates the greatest accuracy of 95.6%, followed closely by SVM of 93.2%, DT of 88.7%, and RF of 86.5% These performance indicators indicate the robustness and accuracy of the model in forecasting environmental conditions for irrigation highlighting the positive. The effectiveness of each model is further demonstrated by confusion matrices, which provide instances of true positives, false positives, true negatives, and false negatives. Achieving a successful integration of IoT and machine learning ensures a proactive response to changing field conditions and lowers the risk of resource misuse through precise adjustments to the water supply. The results emphasise the potential of technology-driven solutions to increase precision agriculture, leading to sustainable practices that optimise yields while conserving resources. The study offers vital insights for agricultural stakeholders, opening the road for flexible and adaptable solutions that solve the problems of contemporary rice production in the face of altering climates and global food.
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