Crop Yield Prediction with Deep Learning

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

Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem [CVPR 2019, oral]

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

Code complementing our manuscript on the prediction of chemical reaction yields (https://iopscience.iop.org/article/10.1088/2632-2153/abc81d) and data augmentation strategies (https://doi.org/10.26434/chemrxiv.13286741).

ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.

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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Repository for the BioCLIP 2 model project. [NeurIPS'25 Spotlight] BioCLIP 2 is a biological foundation model trained on TreeOfLife-200M. Despite the narrow training objective, BioCLIP 2 yields extraordinary accuracy when applied to various biological visual tasks such as habitat classification and trait prediction.

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.

Stock market prediction is the method of trying to determine the future value of publically listed company stock traded on an exchange. The successful prediction of a stock's future price could yield significant profit. Stock market prediction is one of the most important issues to be investigated. Quarrying tweets and time series concurrently, such as predicting the movements of stock prices based on the content of Twitter corpus, is an emerging topic in Machine Learning.

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 Crop Management System is a machine learning-based project designed to provide predictions and recommendations for farmers.

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.

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.

Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales

codes for RS paper: Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

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Crop Yield Prediction Using Machin Learning Python

Crop yield prediction on remote sensing data using CNN