🌱 Crop Yield Prediction using Machine Learning

Implementation of Machine learning baseline for large-scale crop yield forecasting

A tool which is capable of making predictions of cereal and potato yields for districts of the Slovak Republic.

This project aims to design, develop and implement the training model by using different inputs data. The machine will able to learn the features and extract the crop yield from the data by using data mining and data science techniques.

Harness the power of machine learning to forecast rice and wheat crop yields per acre in India, aiming to empower smallholder farmers, combat poverty and malnutrition, utilizing data from Digital Green surveys to revolutionize agriculture and promote sustainable practices in the face of climate change for enhanced global food security.

Prediction of crop yields based on climate variables using machine learning algorithms

Explore our tools to make informed agricultural decisions.

A Mobile and Web application using which farmers can analyze the crops yield in the given set of environmental conditions

ML for crop yield prediction project that was part of my research at New Economic School

Crop Yield Prediction using various ML approaches - Random-Forest Regressor, Gradient-Boosting Regressor, Decision-Tree Regressor, Support-Vector Regressor

Machine Learning based Crop Yield Prediction

Multiservice app with Crop Yield Prediction built using Django , React and Node.

Farmer assistant system VCET Hackathon 2k22

Ridge regression to forecast wheat yield variabilities for Brazil using observed and forecasted climate data.

This is the ORCHIDEE-CROP model used in the paper "Future warming increases the chance of success of maize-wheat double cropping in Europe". For installing ORCHIDEE-CROP model, including the calculation environmental setting, please visit: https://forge.ipsl.jussieu.fr/orchidee/wiki/Documentation/UserGuide

Random Forest Algorithms to predict climate impact-drivers (CID), a.k.a., climate extreme indices for impact studies, in crop yields of soybean maize using Random Forest and XGBoost in a SHAP (SHapley Additive exPlanations) framework