Machine Learning Based Water Requirement Prediction for Agriculture Before a Rainfall

Enhancing crop output and streamlining irrigation techniques can be achieved through the potential application of artificial intelligence (AI) in agriculture. The use of artificial intelligence (AI) approaches to forecast crop irrigation needs before notable rainfall occurrences are examined in this work. The goal is to develop a predictive model that can precisely estimate agricultural irrigation demands based on expected precipitation by using machine learning algorithms and weather data. To provide accurate irrigation suggestions, the suggested AI system takes into account past weather patterns, soil moisture levels, crop traits, and other pertinent parameters. By taking a proactive stance, farmers may minimize water waste, save important resources, and make well-informed decisions about irrigation management.