• Pradyumna D P
• Azad Singh S
• Barakat Ali Barakat Hasan
• Preeyanka S Amin
• Amitha S K

Agriculture is individual of the bigger contributors to worldwide frugality, and it is detracting to have a system that helps producers form cognizant decisions about the crops they can evolve on the site. With this in mind, we propose a plan that uses differing determinants such as precipitation, hotness, dampness, soil moisture, and water chance to anticipate whether a locale is suitable for increasing edible grain. The system will be created to take into account the detracting necessities for farming, which contain determinants in the way that climate, soil type, water chance, and additional material variables. Using machine learning methods, bureaucracy will produce predictions established by the recommendation dossier provided for one consumer, admitting farmers to form conversant resolutions about the best crops to evolve in their particular neighborhood. The proposed whole will be accessible as a netting application that may be achieved from some ploy with cyberspace relates. The use will be designed expected handy, providing a smooth-to-use interface for consumers to recommend their district details, containing their premature period's data or face guidelines. Based on these inputs, the ML model will resolve the data and determine an adept harvest. The ML model that powers bureaucracy will be a buxom utilizing advanced algorithms and rules that have existed and grown by specialists in the field. These algorithms will be planned to increase the forecasting of the crop that is best adapted for the area, taking into account all the determinants that can influence crop tumors. The system will use an alliance of supervised and alone education methods to produce accurate indicators. One of the meaningful benefits of the projected system is that it will be adaptable and flexible, admitting farmers to recommendation dossiers from their distinguishing locations and sustaining a custom-made harvest. The application will still be devised and expected scalable, so it can adapt to a growing number of consumers and a larger capacity of dossier over the period.

Accuracy prediction, Yield Prediction, Rice, Farming, Machiene Learning

"RICE YIELD PREDICTION AND ACCURACY IN KARNATAKA USING DATA MINING AND MACHINE LEARNING TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.b646-b655, May-2023, Available :http://www.jetir.org/papers/JETIR2305185.pdf

"RICE YIELD PREDICTION AND ACCURACY IN KARNATAKA USING DATA MINING AND MACHINE LEARNING TECHNIQUES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppb646-b655, May-2023, Available at : http://www.jetir.org/papers/JETIR2305185.pdf