soil-analysis

All-in-one ag-platform for crop insights, marketplace services, and expert farming consultation.

Plataforma de análisis satelital para monitoreo de suelos agrícolas · Procesamiento de imágenes multiespectrales, índices de vegetación y modelos predictivos.

THE MRIDANSH is a physics-guided AI system that estimates the current best scientific state of soil from multi-modal Earth observation data, providing a unified foundation for agriculture and civil engineering applications.

This innovative system utilizes machine learning algorithms to provide farmers with personalized crop recommendations based on their specific climate, soil type, and regional conditions. Project Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

AI-Powered Agricultural Intelligence Platform | Crop yield prediction, pest detection, soil analysis & market insights for Indian farmers | Next.js 15 + Google Gemini + TypeScript

This repository contains pre-trained machine learning models for crop recommendation based on soil and environmental parameters. The models help predict the best crop to grow based on nitrogen, phosphorus, potassium levels, temperature, humidity, pH, and rainfall data.

AgriGrow Sense is a prototype handheld soil scanner bringing precision agriculture tools to gardeners, homesteaders, and farmers. It measures soil health, maps samples via GPS, and combines open hardware with future AI to democratize soil science.

AI-powered chatbot for farmers with smart crop recommendations and plant disease prediction using machine learning.

GIS-based enhanced farm viability assessment using iSDAsoil, Landsat 8 NDMI and SQI for smallholder farmer onboarding For Kachia LGA, Kaduna State, Nigeria

ML-powered crop recommendation system using Flask. Suggests optimal crop based on soil NPK values, temperature, humidity, pH, and rainfall.

Developed a real-time Crop Recommendation System using Flask, Python, and Machine Learning. The system analyzes key soil and atmospheric parameters to predict the most suitable crop for cultivation. Integrated and evaluated multiple classifiers with Bayesian optimization and visualized performance through a confusion matrix heatmap.

Software de processamento de laudos de análise de solo voltado para a cultura da soja a partir do manual de adubação e calagem para os estados do RS e SC de 2016

AgriAssist is an AI-powered smart agriculture web application that helps farmers with weather updates, soil analysis, crop recommendations, and market price information through a simple and user-friendly interface.

A point-based machine learning model to predict whether there is a rocky terrain or not.

Full-stack AI platform for precision agriculture using Rhizobium-based biofertilizers. Features ML-powered dosage prediction, real-time soil analysis with ESP32 sensors, and Gemini-powered agricultural chatbot.

Experiments chain of thought prompting for pH regression in a cold start setting

Building Crop predicting App

Tarım verilerini analiz ederek toprağa en uygun mahsulü öneren makine öğrenimi modeli. Scikit-learn tabanlı yapısı sayesinde veri odaklı tarım kararlarına destek sağlar.

AI-powered agriculture decision support platform for crop, fertilizer, soil, and plant disease recommendations, built with FastAPI, Scikit-Learn, and web technologies.

Clasificacion de fertilidad del suelo para plantio de la vid en Baja California. Proyecto de investigacion aplicada.