There are 1 repository under agricultural-modelling topic.
crop classification using deep learning on satellite images
Set of Machine Learning Algorithms developed with the aim of determining health states of different types of crops
Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R
Agricultural Monitoring exploiting Sentinel 1 and Sentinel 2. SandboxNL contains detailed explanations about the creation and usage of the parcel based Sentinel datasets.
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.
Estimating shapes and volumes of Capsicum fruits (bell pepper) by fitting superellipsoids to 3D mapping data for autonomous crop monitoring tasks for ROS1
Materials for NCEO crop modelling, Earth Observation and DA workshop in Accra
Caloric Suitability Index
A basic simulation of a coffee operation.
Correlation for African Soil between chemistry and fertility data using Logistic Regression. Treatment of infrared (FTIR) spectra by machine learning.
Processor showcasing how to compare vegetation index before and after an event to determine impacted areas.
A website that allows farmers to input their soil and location details and get a recommendation for the crop to grow and fertilizer to use, using machine learning models.
MOVED: Development of this project continues at
Developed an android mobile app (GreenFinder), trained, and evaluated two deep learning image classification models for the use-case. The mobile app classifies scanned fruits, vegetables and flowers, as well as provides knowledgeable information on each classified item.
A demonstration platform designed for agricultural extension services, facilitating outreach and providing a hub for seeking assistance.
Use this repository as a baseline to Build Your Own Analytic based on metrics and imagery data following your business logic.
The traditional in-situ soil analysis methods are laborious & inefficient, limiting scalability and hindering timely access to crucial soil data for optimal fertilization by farmers. In the amazing challenge, we tried to predict soil parameters(Phosphorous, Potassium, Magnesium and pH)from hyperspectral satellite images.
Using Genetic programming, an Evolutionary Algorithm, to solve and research the problem of Symbolic regression analysis and Rice Classification.
🌾 OWL Ontology for the EURAKNOS project about EU agricultural resources
Predicting the best suitable crop based on various parameters. The model is based on Random Forest Algorithm
Aquacrop-OSPY implementation to analyze the effects of changing irrigation schedules and irrigation depths on key crop parameters.
Plant disease detection bot
Harmonize heterogenous spatiotemporal gridded agriculture-related datasets. Part of a larger ongoing project to monitor land and water use by combining irrigation and gridded data via remote sensing data with machine learning.
Live
Innovation in agriculture productivity and gaining time (Smart irrigation system)
A toolkit for geospatial crop simulations
A machine learning application that detects diseases in cotton plants using image analysis and convolutional neural networks (CNNs). Built with Flask for a user-friendly interface and fast, accurate disease classification.