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.

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

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

This project is build during HACK4TKM hackathon held at TKM College of Engineering on February 2024

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.

Simulation modelling of crop diseases using a Susceptible-Exposed-Infectious-Removed (SEIR) model in R

Estimating shapes and volumes of Capsicum fruits (bell pepper) by fitting superellipsoids to 3D mapping data for autonomous crop monitoring tasks for ROS1

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

An app made for Smart India Hackathon 2018. An idea to help farmers of India to sell their products at much higher rate and with ease. With a vision to positively impact agriculture industry in India.

Predicting the price of agricultural

Predicting the best suitable crop based on various parameters. The model is based on Random Forest Algorithm