Optimizing Water Consumption in Crop Production using Clustering and Machine Learning Models: A Case Study for Arid Regions

Water is a precious commodity, and as the population grows, its importance increases. To solve this problem, a data-driven approach is used to predict the optimal water use for agriculture. The focus of this research is Rajasthan, a state of India known for its arid and semi-arid areas, which have severe water scarcity. This research uses machine learning techniques to analyze a variety of data sets, including soil characteristics, crop production, weather and water use. The goal is to determine the water needed for different crops based on soil conditions, climate and available water resources. The results provide practical recommendations for improving Rajasthan's water management and crop selection. The article concludes with future research proposals and agricultural policy and practice implications.