Analysis and High Accuracy Prediction of Coconut Crop Yield Production Based on Principle Component Analysis with Machine learning Models

Abstract

In analysis of crop yield production, an emerging research field is the Data Mining. Crop yield is a highly
complex trait in agriculture. Basically, data mining is a method for analysing data from varied viewpoints and
summarized the same into important information. For crop yield prediction, Machine learning is also a
significant decision support tool that includes supporting decisions upon, which crops to cultivate and what
actions should be taken while growing season of the yields. The results of the prediction will be made available
to the farmer. For the research of crop yield prediction, various machine learning models have been employed.
In this article, the prediction has been done for coconut crop. This paper applied four different supervised
techniques like Random Forest, Gradient Boosting, Support Vector Machine, and Decision Tree Regression
techniques to get the accuracy of coconut crop yield. This study proposed and implemented all the models to
predict coconut crop by using the previous data. The outcomes of simulation illustrates that the proposed work
efficiently for prediction of coconut crop.