There are 8 repositories under crop-recommendation topic.
A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
AI-based voice-assisted Contact Center for assisting Farmers for their problems.
An application that provides complete assistance to farmers right from sowing to harvesting. Its features include plant disease detection, crop recommendation, real-time API support for environment analysis, detailed crop-cost analysis, buy/sell/rent farming equipment and an interactive farmers' community.
A platform of machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.
A machine learning model in python that recommends the best crop to grow based on Soil composition, Ph level, rainfall and geographical location.
Developed a machine learning-based crop prediction model to assist farmers in making informed decisions about crop selection, planting, and harvesting.Integrated weather and geolocation APIs along with a web page for simplified user experience.
AI Powered Smart Farming Assistant uses advanced technology, including machine learning and CNNs, to provide farmers with crop recommendations, disease identification, weather forecasts, fertilizer recommendation, and crop management guidance through a user-friendly web app.
Saathi - Crop recommendation using ML and plant disease identification using CNN and transfer-learning approach
Precision Agriculture based ML and DL project (local host deployment) which have features of crop recommendation, fertilizer recommendation and pesticide recommendation.
The Crop Management System is a machine learning-based project designed to provide predictions and recommendations for farmers.
The model focuses on predicting the crop yield in advance by analyzing factors like district (assuming same weather and soil parameters in a particular district), state, season, crop type using various supervised machine learning techniques. This helps the farmers to know the crop yield in advance to plan and choose a crop that would give a better yield.
AgriCrop is a Crop Recommendation System built on Machine Learning techniques to recommend the best fitted crop for the given condition by the user.
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
An Intelligent Crop Recommendation system using Machine Learning that predicts crop suitability by factoring all relevant data such as temperature, rainfall, location, and soil condition. This system is primarily concerned with performing AgroConsultant's principal role, which is to provide crop recommendations to farmers.
Helping Farmers make informed decisions with Machine Learning ! 👩‍🌾🚜
We propose an Intelligent Crop Recommendation and Yield prediction system using Machine Learning that predicts crop suitability by factoring all relevant data such as temperature, rainfall, location, and soil condition. The Yield is predicted based on the parameters of area of land available, agricultural season and the past observations of yield .
"Kisaani" is an application that takes required parameters intelligently or from the database of the location (from the cloud) and provides the list of best crops suited for that land. The application should also be able to collect the outcome after cultivation and apply correction as appropriate for further advisories. The details of the crops for the region and conditions are provided. Applications should be interactive, user friendly for farmers (provide local language support) and should provide support in real time.
Explore our tools to make informed agricultural decisions.
Smart Irrigation System
An application for Farmers to recommend them the best types of crops which can be cultivated on a certain piece of land using Soil Image.
Multiservice app with Crop Yield Prediction built using Django , React and Node.
"SowEasy " is a Crop Recommendation system that compares various machine learning models and provides the best crop to plant depending on various factors.
Crop Recommendation system using Machine Learning that predicts crop suitability by factoring all relevant data such as temperature, rainfall, location, and soil condition. This system is primarily concerned with performing Agro Consultant's principal role, which is to provide crop recommendations to farmers.
FastAPI backend for CropFusionAI
Farmer assistant system VCET Hackathon 2k22
Crop Recommendation System using Random Forest Algorithm in Partial Fulfillment of the Final Project on MAN206 Predictive Modelling and Machine Learning
A machine learning model to recommend suitable crops based on soil health conditions.
Leveraging machine learning algorithms for accurate crop yield prediction and price estimation based on temperature, rainfall, humidity, NPK, location
This project aims to develop a crop recommendation system using a Random Forest machine learning model. The system uses a dataset containing information about soil type i.e. PH value and weather factors like temperature, humidity and rainfall to recommend the most suitable crops for a given location.
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
Agro companion is a soil classification, crop recommendation and crop information website. It helps identify seven types of soils, recommend 20+ crop based on specific factors and also help search information required to grow them.
Cultivating yield with an intelligent crop recommendation system