Prediction of Irrigation Water for Plants Using Machine Learning Algorithms

Water is essential for nurturing plants and ensuring high yields. With optimal plant growth being fundamental, managing irrigation challenges due to lifestyle changes is crucial. Crops have varying water needs, some constant, others minimal. Precision farming and automated irrigation systems are necessary to overcome setbacks. Leveraging predictive techniques and considering factors like soil moisture and temperature optimize water usage, increasing crop yield. Machine learning methodologies like Support Vector Machines (SVM), Random Forest, Stochastic Gradient Descent (SGD), Naive Bayes, and Decision Trees offer effective forecasting. Additionally, deep learning mechanisms, particularly Feed-Forward and Long Short-Term Memory, provide valuable insights into water needs of plants. While neural network models greatly impact prediction analysis, machine learning models like Random Forest and Decision Trees exhibit higher efficiency in swift forecasting. Through evaluation of metrics including accuracy, recall, precision, F1 score, execution time, and efficiency of water prediction can be assessed, thereby enhancing automated irrigation practices.