Predictive Analysis of Machine Learning Frameworks in the review of Multivariate datasets for Crop Yield Prediction

Abstract

Crop Yield Prediction (CYP) is crucial for optimizing agricultural practices globally. This study conducts an in-depth review of Machine Learning (ML) techniques applied to multivariate datasets for crop yield forecasting. We examine the performance of models including deep learning, ensemble classifiers, and hybrid approaches, emphasizing their ability to integrate diverse data sources and adapt to varying climatic conditions. Despite significant progress, challenges such as sparse data handling and generalization across regions persist. This paper not only identifies these challenges but also proposes advanced solutions for improving prediction accuracy, offering a roadmap for future research in agricultural ML frameworks.