Performance Evaluation of Machine Learning Algorithms for Smart Irrigation Systems

Abstract

Smart irrigation systems have revolutionized the farming industry by utilizing modern innovations to optimize water usage and handle water scarcity issues. The performance of two machine learning algorithms, Decision Tree and Support Vector Machines (SVM), in categorizing irrigation status in smart irrigation systems is evaluated in this research. The goal is to detect whether or not specified areas or plants have been watered, which is critical for proper irrigation management. Sensors in smart irrigation systems detect environmental data such as temperature, humidity, soil moisture, and rainfall. The obtained data is processed using machine learning methods to classify the irrigation status. For training and evaluation, this study makes use of a large dataset from a real-world smart irrigation system. The results demonstrate the effectiveness of both the Decision Tree and SVM algorithms. Decision Tree excels in terms of precision and recall, allowing for the accurate detection of hydrated and non-watered areas or plants. SVM achieves good accuracy and F1-score, allowing for a complete evaluation of irrigation status. These findings promote smart irrigation systems by emphasizing the importance of machine learning methods for accurate irrigation status classification. The findings help stakeholders choose appropriate algorithms for efficient water management and support sustainable agriculture practices.