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.
A Mobile and Web application using which farmers can analyze the crops yield in the given set of environmental conditions
🌱 Crop Yield Prediction using Machine Learning
A tool which is capable of making predictions of cereal and potato yields for districts of the Slovak Republic.
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
Ridge regression to forecast wheat yield variabilities for Brazil using observed and forecasted climate data.
A data-driven project that predicts crop yield using machine learning algorithms including Support Vector Regression (SVR), Random Forest, Neural Networks, and KNN. The pipeline covers data cleaning, exploratory data analysis (EDA), visualizations, and model evaluation to forecast yield trends. Designed to support farmers.
Farmer assistant system VCET Hackathon 2k22
Implementation of Machine learning baseline for large-scale crop yield forecasting
A web application created to predict the crop yield based on historical data. It can perform basic analysis, along with plotting the crop harvest in various states.
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.
Multiservice app with Crop Yield Prediction built using Django , React and Node.
Machine Learning based Crop Yield Prediction
Explore our tools to make informed agricultural decisions.
Machine learning-based wheat yield estimation in Ukraine using MODIS and ERA5 Land data, with a comparative analysis of six regression models
Harvest Sense: AI-Powered Crop Yield and Soil Quality Prediction
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