This GitHub project implements a crop yield prediction model using multilinear regression, aimed at providing farmers and agricultural stakeholders with valuable insights for decision-making …

This project predicts crop yield using a linear regression model built on historical data. Users can input environmental parameters via a professional web interface. The app provides: Data …

This project focuses on building a predictive system for agricultural crop yield using machine learning models. By leveraging historical data on weather, pesticides, and other environmental …

Our generated outputs show that RF is an effective and adaptable machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of …

Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute …

In this Machine Learning project, we develop a crop yield prediction using the Gradient Boosting algorithm with Python

To choose acceptable features from a data collection for crop prediction, this paper introduces a novel FS approach called modified recursive feature elimination (MRFE). Using a ranking …

Multi linear Regression is used for prediction since yield has to be predicted considering multiple independent variables. Similar studies were carried out in predicting the crop yields using …

Jan 1, 2023 · By taking into account several variables, machine learning algorithms can help farmers decide which crop to grow in addition to increasing yield. Farmers can benefit from …

May 11, 2023 · Crop prediction is done by classification model and yield prediction uses regression models to learn from the data. Multiple ML models are analyzed based on …