TSYS School computer scientist Mohamed Riduan Abid colleagues from Moulay Ismail University, Al Akhawayn University, the University of Houston and Alfaisal University recently coauthored a book chapter on machine learning for Cloud and IoT-based smart agriculture. The chapter points out that predicting soil moisture in smart agriculture is essential for deploying water-efficient irrigation systems. According to Abid, "Various techniques and models have been used to forecast soil moisture. In this study, we looked at the available approaches . . . [using] real-world data from a smart farm prototype . . . to train and validate the suggested models . . . These models were then used to predict data related to soil moisture value." Even though the researchers' model's prediction accuracy was modest, they still recommend that it be used as a blueprint for real-world smart agriculture test beds. Lastly, Abid and coauthors also recommend the use of machine learning to better predict soil moisture. Their chapter appears in Advances in Control Power Systems and Emerging Technologies, which is published by Springer as part of an interdisciplinary series for sustainable development.
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