A crop recommendation API using a machine learning with an accuracy of 99.18%.
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Updated
Jun 25, 2024 - Python
A crop recommendation API using a machine learning with an accuracy of 99.18%.
SmartCrop is an AI model designed to assist farmers in determining the most suitable crop to cultivate based on environmental and soil parameters.
3 different machine learning algorithms based crop recommendation system
ML based Smart Crop Recommendation System with Disease Identification, utilizing CNNs. It aids farmers in selecting crops, managing diseases, and boosts productivity by integrating weather and geolocation APIs.
AClimate official website
Crop Recommendation ML Model | Python
Cultivating yield with an intelligent crop recommendation system
Crop Recommendation
Some AIML Projects
A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.
Farmify is a Python-based project designed to help farmers with crop disease prediction, crop recommendation, and fertilizer suggestions. It utilizes machine learning models and Flask for web application development.
A machine learning model to recommend suitable crops based on soil health conditions.
A neural network-based crop recommendation system leveraging soil and environmental data. Achieved 98% accuracy through hyperparameter tuning and evaluation of two architectures with 2 and 5 hidden layers.
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.
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.
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.
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