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

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.

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.

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

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.