Agriculture data interoperability toolkit for .Net

Set of Machine Learning Algorithms developed with the aim of determining health states of different types of crops

Centroid-UNet is deep neural network model to detect centroids from satellite images.

Data in Climate Resilient Agriculture (DiCRA) is a collaborative digital public good which provides open access to key geospatial datasets pertinent to climate resilient agriculture. These datasets are curated and validated through collaborative efforts of hundreds of data scientists and citizen scientists across the world.

Android app for plant disease identification using ML

The Smart Agriculture Advisory System is an application designed to provide farmers with personalized advice on crop management, pest control, and irrigation scheduling. By leveraging machine learning models, the system analyzes various environmental and soil parameters to recommend the most suitable crops for cultivation.

Predicting rice field yields through the integration of Microsoft Planetary satellite images, meteorological data, and field information in the 2023 EY Open Science Data Challenge - Crop Forecasting.

Open Source Content Management System for National Meteorological and Hydrological Services

ADAPT Framework plugin for ISOXML

[ACL 2025] SeedBench: A Multi-task Benchmark for Evaluating Large Language Models in Seed Science🌾

Standard data schema for agriculture interoperability

The ISO 11783 standard specifies a serial data network for control and communications on forestry and agricultural machines. Every control function in an ISOBUS network has a unique NAME ( defined in 11783-5) that contains information about the manufacturer code, the device class and other common fields. The information is encoded in a byte value representation.

AI disease detection and prediction for humans, plants, and animals. Complete ML project with custom training, offline operation, no API keys. Detect diseases from images using deep learning and computer vision. Open-source disease detection system for healthcare, agriculture, and veterinary applications. Full code and deployment guides.

CartoBio API

The ISO 11783 standard specifies a serial data network for control and communications on forestry and agricultural machines. Every device in the ISOBUS network has a label given by device to identify the device descriptor localization. ( defined in 11783-10) that contains information about the lanaguage command PGN defined in ISO 11783-7. The information is encoded in a byte value representation.

Cleaned and constructed indicators based on the LSMS-ISA Data. Source code for producing the estimates is available at https://github.com/EvansSchoolPolicyAnalysisAndResearch/LSMS-Agricultural-Indicators-Code

AgriFarm, is a web application that aims to optimize the agricultural supply chain by connecting farmers, middlemen, cold storage facilities, and customers. The platform will leverage machine learning algorithms to predict the demand for various agricultural products and provide valuable insights to farmers to plan their production accordingly.

This repository includes code for constructing a variety of agricultural development indicators from household survey microdata (primarily LSMS-ISA surveys) as well as documentation for construction decisions across instruments.

{extractOz}: Get local attribute information from GPS points for Australian agricultural research

Serialization plugin for the ADAPT Framework

Farmioc | Global Agriculture Data and Agri Trading Company

Harvest data from Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) part of the Australian Department of Agriculture, Fisheries and Forestry and the Australian Bureau of Statistics (ABS) for your work in R

No description provided.

Spreadsheet-based approach for managing data from agricultural field experiments with emphasis on best management practices. Initial version is focused on Florida but could easily be adopted to other types of field research. Includes various tools for managing datasets.

Python library for data extraction from drone orthomosaics in plant breeding trials.

DigiFarmer is a modern, AI-powered web application that revolutionizes agricultural decision-making by combining advanced computer vision and machine learning technologies. Upload a soil image and receive intelligent crop recommendations based on soil classification and environmental analysis.

Predicts Agricultural Produce based upon area and year as the features

Computer vision and satellite imagery analysis for precision agriculture. Detects crop diseases, monitors soil health, predicts yields, and optimizes irrigation using drone imagery and multispectral data.

A human and machine-friendly web interface for Lexicon

libadf - Agriculture Data Format