crop classification using deep learning on satellite images

Sen4AgriNet: A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning

[WACV 2023] Information and scripts for the CropAndWeed Dataset

Deep-Plant: Plant Classification with CNN/RNN. It consists of CAFFE/Tensorflow implementation of our PR-17, TIP-18 (HGO-CNN & PlantStructNet) and MalayaKew dataset.

Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020

[RSE 2021] Crop mapping from image time series: deep learning with multi-scale label hierarchies

Code for the paper Multi Modal Deep Learning Based Crop Classification Using Multispectral and Multitemporal Satellite Imagery published in KDD Applied Data Science Track 2020

EuroCropsML is a ready-to-use benchmark dataset for few-shot crop type classification using Sentinel-2 imagery.

A dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.

Public repository of our IGARSS 2023 submission

Crop Classification of Remotely Sensed Images containing Multi Temporal and Multispectral Information

Python data pipeline to acquire, clean, and calculate vegetation indices from Sentinel-2 satellite image. Package is available only for our clients.

Crop classification in the Cauvery Delta Zone using a multichannel based transformer model

Public repository of our JAG paper

Crop classification from Sentinel-2 imagery using Graph Convolutional Networks (PyTorch Geometric) | 99.9%% accuracy | Interactive Streamlit demo

Preprocessing and harmonization scripts for IACS/GSA data.

No description provided.

Public repository of our IGARSS 2023 submission

Machine Learning classification of cropland vs non-cropland using Sentinel-2 satellite imagery and vegetation indices. Achieving 100% accuracy through spectral analysis in Ghana's Brong-Ahafo region. Built with Python, Scikit-learn, and Google Earth Engine.

Multi-headed CNN for simultaneous potato/tomato classification (99.9% accuracy) and quality assessment (98.5% accuracy). Features Grad-CAM explainability, Streamlit interface, and 30% parameter reduction. Built for precision agriculture with real-time crop monitoring.

This Fiboa extension enables validation against the Hierarchical Crop and Agriculture Taxonomy (HCAT), which harmonizes all declared crops across the European Union.

🌾 Classify crops using Graph Convolutional Networks for accurate pixel-level analysis, enhancing agricultural insights with cutting-edge machine learning techniques.

Multi-class crop classification in Elgabel Region, Sudan using Sentinel-2 imagery with scikit-learn (MLP, XGBoost, Random Forest) and PyTorch deep learning (CNN1D, Hybrid CNN+MLP, Transformer). Achieves 100% accuracy with FocalLoss, SMOTE, and class weighting.

Source code from 2022 AI CUP Competition on Crop Status Monitoring by Image Recognition.

No description provided.

A complete preprocessing and machine learning pipeline for crop classification using multispectral UAV orthoimages and temporal analysis.

CNN-based crop classification using Sentinel-2 and MODIS sources for Deltares