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.

Predicting crop yields is crucial to agriculture. Crop production is affected by a number of factors. The goal of this study is to provide low-cost techniques for forecasting agricultural yields utilising existing variables like irrigation, fertiliser, and temperature. The five Feature Selection (FS) algorithms described in this article are sequential forward FS, sequential backward elimination FS, correlation-based FS, random forest variable significance, and the variance inflation factor algorithm. Machine learning techniques are typically well adapted to a particular area, therefore they substantially help farmers forecast agricultural output. With a novel FS method termed modified recursive feature removal, crop prediction can be improved (MRFE). The MRFE approach locates and ranks the most crucial characteristics in a dataset with the use of a ranking algorithm