Sustainable Water Management in Precision Agriculture

This paper uses machine learning models to optimize the utilization of water in precision agriculture. The main objective is to create a dependable system that can forecast the ideal irrigation conditions, increasing sustainability and efficiency. The process entails the thoughtful placement of sensors, the creation of a wireless network of sensors (WSN), as well as the examination of a carefully selected dataset that includes vital variables like humidity, temperature, precipitation, wind speed, along with soil moisture. The dataset's integrity is guaranteed by further data pre-processing, which includes operations like cleaning, feature significance analysis, and refining. The 105,217-entry dataset provides the basis for training and assessing machine learning models, including Random Forest, the Decision Tree, and Support Vector Machines (SVM), along with Neural Network. The findings show that the Random Forest model performs better than the other models, with an astounding accuracy of 92.68%. At 91.83% accuracy, the model based on Decision Trees comes a close second. With accuracy levels of 90.78% & 90.21%, respectively, SVM as well as Neural Network models perform competitively. The Random Forest model's accuracy in forecasting ideal irrigation conditions is further supported by the precision along with F1-Score measures. This paper provides a strong foundation for environmentally friendly water use and offers insightful information about using machine learning techniques for precision agriculture.