Abstract: Agriculture is the main pillar of economy in our country. Most of the families rely on Agriculture. Country’s Gross Development predominantly lean on Agriculture. 60%of the land is utilized for Agriculture to adequate the requirements of the Country’s population. To meet the requirements, modernization in Agricultural practices is required. Thus, heading towards the growth in Farmers’ and Country’s economy. This Project is an Attempt to minimize the losses occurs in Agricultural field. The majority of experimenter's work on agribusiness focuses on biological mechanisms to identify crop growth, improve yields, price prediction and plant disease classsification, hence presentation of agribusiness is influenced by several weather aspects.Additionally, the system incorporates crop prediction capabilities to forecast suitable crops based on environmental conditions and historical data. Furthermore, the fertilizer recommendation model suggests optimal fertilizers based on soil composition, crop type, and nutrient requirements. This multifaceted approach facilitates precision agriculture by enabling farmers to make informed decisions regarding crop health management, crop selection, and fertilization practices. The developed crop price prediction and forecasting system helps farmers to predict price of the commodity. The system gives detailed forecast up to next 12 month. The methodology we use in the system is decision tree regression and Random forest which is machine learning regression technique. The integration of these models provides a holistic solution for enhancing agricultural productivity, optimizing resource utilization, and promoting sustainable farming practices.
Keywords: RFA Random Forest Algorithm, Back Propagation, Price prediction, Crop Growth, Improve Yield,Price,Plant Disease Classification
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DOI:
10.17148/IJARCCE.2024.131233
