Abstract
Agriculture is Morocco’s primary sector and depends mainly on rainfall. Therefore, water is an essential resource that must be managed with care. Traditional irrigation methods waste a lot of water, and crops are often under or over irrigated. To guarantee the right quantities of water for plants, automatic irrigation systems are available. They make it possible to ensure the quantities of water necessary for the plant. In this paper we will present an intelligent irrigation system based on Internet of things (IoT) and artificial intelligence (AI). Node-MCU 32S boards were used to monitor physical parameters such as air temperature and humidity, soil temperature and moisture, rain and light. Collected data is routed to the raspberry pi 4 via MQTT protocol, then the program running inside the raspberry determinates how much water is needed to irrigate the plants. Based on that calculated amount, an instruction is sent to Node-MCU 32S boards to operate the pumps connected to the relay module. Weather data history is used to forecast reference crop evapotranspiration \(\left({ET}_{0}\right)\) to predict the amount of water needed during each growing stage using neural networks especially long short-term memory (LSTM) techniques of recurrent neural networks (RNN).
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Mhaned, A., Salma, M., Mounia, E.H., Jamal, B. (2023). Contribution to Smart Irrigation Based on Internet of Things and Artificial Intelligence. In: Lazaar, M., En-Naimi, E.M., Zouhair, A., Al Achhab, M., Mahboub, O. (eds) Proceedings of the 6th International Conference on Big Data and Internet of Things. BDIoT 2022. Lecture Notes in Networks and Systems, vol 625. Springer, Cham. https://doi.org/10.1007/978-3-031-28387-1_45
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