Abstract
Abstract: Agriculture relies heavily on the ability to predict crop yields. When it comes to crop yield, there are a number of factors at play. This research is focused on developing cost effective methods for predicting crop yields using available parameters like irrigation, fertilizer, and temperature. Sequential forward FS, sequential backward elimination FS, correlationbased FS, random forest variable importance, and the variance inflation factor algorithm are among the five Feature Selection (FS) algorithms discussed in this study. In general, machine learning techniques are well-suited to a specific region, so they greatly assist farmers in predicting crop yields. Crop prediction can be improved by using a new FS technique called modified recursive feature elimination (MRFE). With the help of a ranking algorithm, the MRFE technique identifies and prioritizes the most important features in a dataset.
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