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.

Harvest Sense: AI-Powered Crop Yield and Soil Quality Prediction

Sustainable Crop Yield Prediction using Machine Learning

An AI-driven solution that uses data insights to help farmers increase their productivity and efficiency.

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.

A project to estimate Finger Millet production using data analysis and prediction models — includes data preprocessing, visualization, and yield forecasting.

Machine learning-based wheat yield estimation in Ukraine using MODIS and ERA5 Land data, with a comparative analysis of six regression models

Edge AI Prototype enhances sustainability via AI. Task 1 classifies recyclables (Organic and Inorganic) using MobileNetV2. Task 2 predicts crop yields with Random Forest. Task 3 analyzes AI ethics in medicine. Uses TensorFlow, Scikit-learn, Pandas, and TFLite.

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

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

This is the source code of ORCHIDEE-CROP model and other related models developed based on ORCHIDEE-CROP model. For installing ORCHIDEE-CROP model, including the calculation environmental setting, please visit: https://forge.ipsl.jussieu.fr/orchidee/wiki/Documentation/UserGuide

Farmer assistant system VCET Hackathon 2k22

🌾 CropCadet is a smart assistant for farmers and agri-enthusiasts, simplifying crop planning, monitoring, and decision-making through 🤖AI-powered insights.

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

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

Machine Learning based Crop Yield Prediction