Implementing Machine Learning to Predict Plant Irrigation Needs in an IoT-Based Smart Agriculture System
Keywords:
Smart Agriculture,, Machine Learning, Internet of Things, Irrigation Prediction, Smart FarmingAbstract
The development of Internet of Things (IoT) and Machine Learning technology has opened up opportunities in the development of Smart Agriculture to improve the efficiency of agricultural resource management, particularly in crop irrigation systems. Conventional irrigation management often leads to water waste and reduces crop productivity. The purpose of this study is to implement Machine Learning in predicting crop irrigation needs in an Internet of Things-based Smart Agriculture system so that water use can be carried out more effectively and efficiently. The research method uses a quantitative method with a system development approach that integrates IoT sensors and Machine Learning algorithms. Environmental data is obtained from soil moisture, air temperature, and humidity sensors collected in real-time, then through preprocessing, model training, and testing to produce predictions of crop irrigation needs. The results show that the system successfully collects and processes environmental data automatically through IoT devices. The Machine Learning model is able to analyze the relationship between soil moisture, air temperature, and air humidity to produce predictions of crop water needs in real-time. The integration of IoT and Machine Learning enables the continuous land monitoring process and helps determine the timing and amount of irrigation more precisely than conventional methods. The implementation of Machine Learning in an IoT-based Smart Agriculture system can support decision-making in irrigation management more accurately and efficiently. The developed system has the potential to increase agricultural productivity while optimizing water resource use. Future research could focus on developing more complex predictive models utilizing more diverse environmental data to improve the system's accuracy.
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