Abstract
Water is more than just a necessity to sustain life on the planet by quenching the thirst of humans, animals and plants, There are many reasons why we may face a worse global water crisis in the future than we are currently experiencing. Among the most important of these reasons is the loss of large quantities of fresh water during the irrigation process. In this paper, we present a new irrigation technique that focuses on studying the stages of plant development and estimating the actual amount of water needed at each stage, in order to minimize Over-watering and Under-watering of the plant during its life stages. We use a high amount of data previously gathered through a Wireless Sensor Network (WSN) spread in different places in the agricultural field, then we use k-Nearest Neighbors (KNN) and Weighted-k Nearest Neighbors (W-KNN) to train the Machine Learning model. However, in most existing methods of irrigation the estimated amount of water directed to the plant is constant during all stages. Our proposed solution is able to overcome this disadvantage by introducing the development stages of the plant to the learning model. The results obtained through W-KNN algorithm outperform manual irrigation and automated irrigation without stages.
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Abdelhak, B., Mohammed, K. (2024). Machine Learning Based Intelligent Irrigation System Using WSN. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-031-47721-8_24
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DOI: https://doi.org/10.1007/978-3-031-47721-8_24
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