Banana Irrigation System and Scheduling based on Reinforcement Learning

Banana Irrigation System and Scheduling based on Reinforcement Learning

© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Angelin Blessy, Avneesh Kumar, Prashant Johri
DOI : 10.14445/22315381/IJETT-V70I8P240

How to Cite?

Angelin Blessy, Avneesh Kumar, Prashant Johri, "Banana Irrigation System and Scheduling based on Reinforcement Learning," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 394-400, 2022. Crossref,

Water optimization and scheduling are essential for today's agriculture sector because water and energy usage are not adequately estimated. A tremendous amount of water is wasted in the irrigated fields. The combination of today's technologies provides the solution for managing water and providing the proper irrigation schedule. The Internet of Things and machine learning techniques are effectively used for smart agricultural fields. This paper proposes an effective water optimization and scheduling method that uses IoT components, the KNN algorithm, reinforcement learning, and person correlation techniques. The IoT components are used to collect the current requirements and predict the environmental status of the cultivation files. And is also used to transfer the information from the entire cultivation field to control fields. The KNN algorithm captures the nearest features from the cultivation fields. Environmental prediction, awards, or requirements of specific plants are performed using IoT and KNN capabilities. In this work, we applied a smart irrigation system used in banana cultivation. Based on the current prediction, the future requirements of water are calculated in a 12- hour time interval from 7 pm to 7 am, and it is calculated for up to 4 days. Compared to traditional cultivation, this proposed method reduces water usage by up to 24% of the water required.

Smart Irrigation System, Scheduling, KNN, Reinforcement Learning, IoT, Banana Cultivation.

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