Crop Yield Prediction with Deep Learning

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

This repository contains codes for the paper entitled "A CNN-RNN Framework for Crop Yield Prediction"

AgriTech is an AI-powered web platform that offers crop recommendations, yield prediction, disease detection, and collaborative tools to empower farmers and promote smart, sustainable agriculture.

A simple Web application developed in order to provide the farmers/users an approximation on how much amount of crop yield will be produced depending upon the given input

Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018. Best Presentation Winner.

Prediction of crop yields using machine learning.

Prediction of Crop Yield for farmers based on weather, satellite data

PyTorch Implementation of "MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer", accepted by ICCV 2023.

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Team - AgriOracle - IBM Hack Challenge 2021 - AI-Assisted Farming for Crop Recommendation & Farm Yield Prediction Application

:seedling: Crop Yield Prediction using Machine Learning

The model focuses on predicting the crop yield in advance by analyzing factors like district (assuming same weather and soil parameters in a particular district), state, season, crop type using various supervised machine learning techniques. This helps the farmers to know the crop yield in advance to plan and choose a crop that would give a better yield.

This repository contains my code for the "Crop Yield Prediction Using Deep Neural Networks" paper.

We aim to build an ML model that will predict the yield of a crop using time series analysis of remote sensing data.

Understanding crop yield predictions from CNNs as our final project for CS231N

This is the official github repository of "An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions", accepted by KDD 2024.

The proposed system will be able to predict the crop yield production which will be useful to farmers for harvesting and storage. The system will use the weather forecasting which includes the parameters like temperature, rainfall, humidity, dew point and the normalized difference vegetation index time series from Sentinel-2 satellite for selected region. By using both the results obtained,accurate crop yield prediction can be calculated which will help farmers in planning efficiently, minimize costs and maximize yields—and profits—as a result.

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

This project uses ensemble machine learning techniques to predict crop yield based on climate change scenarios. The models integrate climatic variables such as temperature, rainfall, CO₂ levels, and soil properties to forecast agricultural output.

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Crop Yield Prediction Using Machin Learning Python

Crop yield prediction on remote sensing data using CNN

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Crop Yield Prediction with Remote Sensing uses satellite-derived indicators and machine learning to estimate agricultural productivity. By analyzing vegetation indices, weather patterns, and soil features, the system provides scalable, data-driven yield forecasts that support precision farming.

Code for ICDM 2020 paper Context-aware Deep Representation Learning for Geo-spatiotemporal Analysis

A GNN-RNN approach for harnessing geospatial and temporal information: application to crop yield prediction

Code for replicating "Machine learning methods for crop yield prediction and climate change impact assessment in agriculture", Environmental Research Letters, 2018

Crop Yield Prediction Web App Built using Sklearn and Laravel Web Framework

Prediction of yield and profitability of crop records of India for the agricultural sector using machine learning techniques