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.

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

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

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

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

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.

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.

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.

An AI-driven solution that uses data insights to help farmers increase their productivity and efficiency.

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.