The Smart Agriculture Advisory System is an application designed to provide farmers with personalized advice on crop management, pest control, and irrigation scheduling. By leveraging machine learning models, the system analyzes various environmental and soil parameters to recommend the most suitable crops for cultivation.

A machine learning web application built with Flask that predicts the most suitable crop based on soil nutrients, temperature, humidity, pH, and rainfall. Objective is to recommend the best crop for cultivation based on soil and environmental conditions using a Logistic Regression model trained on the Crop Recommendation Dataset.

KisanSahayak is a smart agriculture web application aimed at providing Indian farmers with data-driven insights using advanced machine learning, rainfall analysis, crop recommendations, and disease prediction.

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

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

Crop, fertilizer, and disease recommendations website based on machine learning models that are trained using very large datasets taken from kaggle

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 project is a Machine Learning based Crop Recommendation System built using Flask. It helps farmers or users decide the most suitable crop to grow based on soil nutrients and environmental conditions.

CROPIFY: An Intelligent System is a Smart Farming Assistant uses advanced technology, including machine learning and CNNs, to provide farmers with crop recommendations, disease identification, weather forecasts, and crop management guidance through a user-friendly web app.

Crop Recommendation and Health management system

KrishiGyaan is a web app designed to help farmers make informed decisions on crop selection. By analyzing soil and environmental factors, the app provides personalized crop recommendations, enhancing agricultural productivity and promoting sustainable farming practices.

AI-powered crop recommendation system using machine learning to help farmers choose the best crop for their soil and environment.

CropWise UK is an AI-driven platform that recommends the most suitable crops for UK cities using soil, climate, and pollution data. It combines machine learning, rule-based reasoning, and geospatial analytics to provide accurate, actionable, and sustainable planting insights for farmers, researchers, and policymakers.

Crop Recommendation using Logistic Regression

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.