Water Treatment and Crop Recommendation Model Using Artificial Intelligence

Modern agriculture has enormous problems due to water constraint and rising food demand. Farmers face unpredictable precipitation and dwindling water supplies as climate change affects weather patterns, demanding creative techniques to optimize water management and increase agricultural output. This research describes a smart agricultural system that combines water treatment with crop suggestion to enhance sustainable farming practices. Potential pollutants and water quality criteria are discovered using real-time data gathering and analysis, allowing farmers to take remedial action and optimize irrigation schedules to reduce water waste and environmental implications. Furthermore, the crop suggestion system analyses environmental data, past crop performance, and soil properties using AI-driven algorithms. The algorithm recommends crop types for a certain location or plot based on parameters such as climate, soil moisture levels, and nutrient content. Farmers can make educated decisions, maximize agricultural productivity, and adjust to changing climatic circumstances as a result of this. The agriculture industry can solve the concerns of water shortages and food security by embracing this technology-driven solution, while also encouraging ecologically conscientious farming practices.