Key points are not available for this paper at this time.
Smart Irrigation System is a complex concept used to control, monitor and automate the irrigation of yields by integrating artificial intelligence techniques such as Machine Learning strategies. SIS has endorsed various machine learning models. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Moreover, ML based SIS often face problems and raise questions that must be resolved. This article presents a systematic literature review of ML based SIS; an overview of the literature on ML is designed, revealing a premier and unbiased survey of the existing empirical research. 55 selected studies published from 2017 to 2023 and nine broadly used ML models were identified. Furthermore, four analysis aspects namely type of ML technique, estimation accuracy, model comparison, and estimation context have been outlined. The findings of this review prove the performance capability of the ML techniques endorsed within SIS. The ML techniques outperform other conventional approaches. However, the application of ML models in SIS is still limited and more effort is needed to obtain well-formed and generalizable results. To this end, and based on the outcomes obtained in this work, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.
Younes et al. (Fri,) studied this question.