Popular GitHub Repositories for Soil Moisture

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

1-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data

https://soilnet.herokuapp.com/ Created a Soil Classification model using Deep Learning which also suggests Crops in 6 different languages and deployed on Heroku. Soil Classification is one of the most important topics, for Farmers and Agriculture Researchers.

A system that is capable of automatically irrigating the agricultural field by sensing the parameters of soil in real-time and predicting crop based on those parameters using machine learning. The system also predicts the yield of the crop.

AI based IoT Smart Farming Solution

Large-scale digital mapping of soil organic carbon content by using machine learning algorithms

Machine learning algorithm in Python that can analyze soil data and determine if it's vulnerable to erosion or not.

LiqueANN : Artificial Neural Network model for predicting soil liquefaction potential.

KrushiSaathi is an AI-powered agri-assistant offering voice-based support, crop and soil analysis, and personalized guidance to empower small and marginal farmers across India.

Computer Vision + AI. Poisonous Plant and Soil Type Detection

A space-filling procedure to generate data from a constitutive model (viscoelastic-viscoplastic-damage) including moisture, strain rate, and nanoparticle volume fraction dependency.

Neural Transfer Learning For Soil Liquefaction Tests

Soil VIS-NIR reflectance spectra simulation based on generative model. PROSAIL model is integrated..

The Smart Farming Assistant is an AI-powered tool that helps farmers make better crop and fertilizer decisions by combining real-time weather and soil data with machine learning. It features a user-friendly web dashboard, live weather integration, and tailored recommendations to boost farm productivity.

Server code for the Agro-Companion website, featuring Flask-based services for deep learning-driven soil classification and fuzzy logic-based crop recommendation

A FastAPI-based application designed to provide various agricultural services, including crop recommendation, soil type detection, interaction with historical data via a language model, and weed detection.

A data-driven ML model to help farmers choose optimal crops based on soil and weather conditions.

OpenGL-based Marble Solitaire game with Blender models and irrKlang audio.

? Smart Agriculture Assistant: NLP-powered system that extracts agricultural parameters (temperature, humidity, moisture, soil type, NPK levels) from natural language input to provide crop and fertilizer recommendations. Built with NER, DeepSeek, Flask, and Docker support.

? Smart Agriculture Assistant: NLP-powered system that extracts agricultural parameters (temperature, humidity, moisture, soil type, NPK levels) from natural language input to provide crop and fertilizer recommendations. Built with NER, DeepSeek, Flask, and Docker support.