A demonstration platform designed for agricultural extension services, facilitating outreach and providing a hub for seeking assistance.

A toolkit for geospatial crop simulations

AI agent with custom plant sensor data input, designed to assist your grow 24/7

Modern Agriculture store website

Comprehensive database for diazotroph nitrogenases, alternative nitrogenases, and nitrogenase-like enzymes at the University of North Carolina at Charlotte (UNCC)

My personal GitHub profile repo!

AgriVision is a project designed to identify cultivatable land for agricultural purposes using real-time satellite imagery and advanced image processing techniques. The project leverages an R Shiny app to provide a user-friendly interface where users can upload satellite images, view processed results, and receive precise crop suitability recommend

Innovation in agriculture productivity and gaining time (Smart irrigation system)

Plant disease detection bot

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.

Caloric Suitability Index

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.

An R package for simulating Generalised Management Strategy Evaluation

Processor showcasing how to compare vegetation index before and after an event to determine impacted areas.

Using Genetic programming, an Evolutionary Algorithm, to solve and research the problem of Symbolic regression analysis and Rice Classification.

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.

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.