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

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

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.

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

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

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

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

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

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

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.

Bot Telegram que analisa laudos de solo com IA (Google Gemini) e gera recomendações de calagem e adubação — baseado em Embrapa Cerrados 🌱

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.

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.

a machine learning-based project that predicts optimal crop types based on soil nutrient and pH levels

Statistical tools for fitting and evaluating soil water infiltration models in R

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

A modular Python tool for Univariate Feature Selection in agriculture. Evaluates soil metrics (N, P, K, pH) independently to identify top predictors for crop classification.