An open-source, low-cost, image-based weed detection device for in-crop and fallow scenarios.
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Updated
Jun 12, 2024 - Python
An open-source, low-cost, image-based weed detection device for in-crop and fallow scenarios.
Factura Electrónica AFIP y otros servicios web (proyecto software libre) — Interfases, tools and apps for Argentina's gov't. webservices (soap, com/dll simil-ocx, pdf, dbf, xml, json, etc.) #python
PyTorch implementation of the model presented in "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention"
The core repository for the FOODON food ontology project. This holds the key classes of the ontology; larger files and the results of text-mining projects will be stored in other repos.
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
Earth Observation Data Analysis Library
Information and scripts for the CropAndWeed Dataset
AgrO describes agronomic practices, techniques, and variables used in agronomic experiments.
Recommendation system for farmers based on soil type, location, season etc.
Multimodal Dataset for Localization, Mapping and Crop Monitoring in Citrus Tree Farms, ISVC 2023
A platform for agriculture smart contracts based on the NEO blockchain.
Implements weather station class in Python that calculates ETo (reference crop's evapotranspiration) based on UN-FAO Irrigation and Drainage Paper 56
An LSTM to generate a crop mask for Togo
Image segmentation of cultivated land
A web portal for connecting farmers and agriculture experts
Oil Palm Growth and Yield Model
Annual and in-season crop mapping in Kenya
Mapping vegetation properties in Google Earth Engine using GPR models and the Sentinel-2 L1C product.
Learning to predict crop type from heterogeneous sparse labels using meta-learning
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