There are 3 repositories under crop-prediction topic.
An all purpose flutter app for farmers made under Food and Agriculture theme in Accelathon hackathon
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
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.
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.
The Crop Management System is a machine learning-based project designed to provide predictions and recommendations for farmers.
SmartCrop: Intelligent Crop Recommendation
A mobile application involving machine learning to recommend crop variety and also predict crop yield.
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.
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
The AI-Driven Crop Prediction System that applies Machine Learning and AI to analyze weather, soil, and crop data to predict crop health and yield. This system provides farmers with precise predictions, empowering them to make data-driven decisions and enhance their farming practices.
This web application uses Machine Learning to recommend crop, fertilizer, pesticide and storage process based on various variables. Algorithm used is SVM for multi-classification
Build@ARSD - Tech for Good
This Github Repository Contains a machine learning powered crop price prediction application with a firebase connected login and signup
Revolutionize your farming with Farmwiser, the ultimate TinyML based Smart Agriculture solution!
"Excited to share my latest project on LinkedIn: a crop yield prediction ML model deployed with Streamlit! 🌱 Leveraging the power of Stochastic Gradient Descent regression(SGD) algorithm, this tech-driven solution boasts an impressive 94% accuracy on both training and testing data.
With this project, we hope to help solve the problems faced during farming and help the farmers, government and consumer by highlighting the advantages of using Machine Learning to predict crop yield and present an alternate supply chain by using block chain and decentralized the entire process.
Deployed ML-Backend Server to predict the best crop you should sow in your fields depending on environment conditions.
Farmer assistant system VCET Hackathon 2k22
Prediction of suitable crop using soil and weather conditions.
METADATA-FARMER ASSISTANCE WEBAPP | AI & ML
Crop Prediction using Machine Learning (Classification Use Case)
This contains only frontend code of the project to run this, you'll have to clone the backend repo in your machine and run the backend with the python scripts first, below is the deployed link where you can check the working of the webApp
ML solutions and other API based features to support Agriculture and Farmers. Goto Wiki or click on below link for Project Report.
App for monitoring crop environmental conditions, allowing the farmer to get insight and take better decisions.
Crop recommendation Web Application using Machine Learning along with fertilizer and cultivation season recommendation made with flask. The Prediction is performed using Random Forest Model
Forecasting crop yields is a crucial element of farming, enabling growers to make well-informed choices regarding their agricultural output. This process entails predicting the quantity of crops expected to be harvested within a specific region, taking into account factors like soil composition, climatic patterns, and agricultural techniques.