Crop Yield Prediction Using Random Forest

Agriculture is the backbone of the Indian economy, with more than half of the country's people relying on it for subsistence. Crop production is predicted using machine learning techniques based on parameters such as rainfall, crop, and meteorological conditions. The most popular and powerful supervised machine learning algorithm, Random Forest, can do both classification and regression tasks. 

They are used in crop selection to reduce crop yield output losses, regardless of the distracting environment. Weather, climate, and other related environmental elements have posed a significant danger to agriculture's long-term viability. Machine learning (ML) is significant since it offers a decision-support tool for Crop Yield Prediction (CYP), which may help with decisions like which crops to cultivate and what to do during the crop's growing season. Crop yield estimation's major purpose is to boost agricultural crop production, and it does so use a variety of well-established models. Machine learning is increasingly widely used around the world due to its success in a range of disciplines such as forecasting, fault detection, pattern identification, and so on. A key agricultural concern is a yield prediction. 

Farmers will be able to determine the yield of their crop before growing on the agricultural field using the results of this study, allowing them to make informed decisions. To assist farmers in maximizing agricultural yield, timely instructions to forecast future crop output and analysis are required.

Keywords: Crop Yield Prediction, Random Forest Algorithm