Executive Summary : | Precision agriculture is a satellite farming method that relies on location-based factors for crop recommendations. This approach uses nature-inspired meta-heuristics to process these factors as inputs, reducing data dependency and allowing farmers to utilize available resources effectively. The system considers recommendations even in dynamic environments, using temporal satellite data, historical data, and scenario development approaches. The proposed system considers larger factors of crop growth and more variety of crops for accurate prediction, leading to the production of an Agriculture Atlas. The use of nature-inspired intelligent CI techniques provides advanced meta-heuristic objective functions that model crop growing parameters given by experts and use their statistical information as initial input. The recommender system learns iteratively and improves itself, leading to more effective solutions to complicated pattern recognition problems. Research gaps include limited factors (mostly climate-based), lack of GIS and remote sensing for feature extraction, and lack of automation or semi-automation. The project proposes machine learning and AI algorithms to automate crop selection processes. The aim is to rank and give recommended crops for a given place, addressing shortcomings such as monotonism in crop growing factors and inability to modify expert knowledge base over time. Main stakeholders are farmers, who will be able to use the system through a mobile app in Hindi. The coverage area is up to tehsil level, covering Jhunjhunu district with emphasis on Chirawa, Surajgarh, and Khetri districts. Cartosat and multiband temporal images will be used. |