Review of Knowledge-Based Management System for Irrigation Scheduling Modeled Upon Reduced Parametric Estimates

Irrigation scheduling is the process of ensuring appropriate, adequate and proportionate crops. Water Management (CWM) stands very important for its water management capability and crop yield optimization among several other advantages. Efficient water management is always crucial for sustainable agricultural practices, traditional irrigation methods often lead to water wastage and suboptimal crop yields Hence, the adoption of technological advancement that spans from the traditional and manual mode to automation, to the application of IOT and extends to the use of Artificial Intelligence (AI). The review paper considers using knowledge-based algorithms for irrigation scheduling, focusing on those that need fewer input parameters. The review looks at several different kinds of knowledge-based algorithms, such as Fuzzy Logic Control, Expert Systems, Neural Networks, Genetic Algorithms, Decision Trees, and Reinforcement learning. The review highlights the fact that knowledge-based algorithms could be a great alternative to traditional irrigation scheduling models, especially when it comes to places where there are few resources for computing power or getting the right data. It also talks about the challenges that come with using these algorithms. Overall, the review makes a strong case for using knowledge-based algorithms for irrigation scheduling. It discusses the tools and techniques used to make these algorithms work well and offers some advice on how to ensure they're being used in the best possible way.