🚀 New Project Proposal: Fertilizer Recommendation System using Machine Learning 🌾
📌 Project Title
Fertilizer Recommendation System using Machine Learning | Agriculture AI
📝 Description
This project is designed to help farmers and agricultural communities by recommending the most suitable fertilizer based on soil conditions, crop type, and nutrient levels. Many farmers struggle with fertilizer selection due to imbalances in soil nutrients and varying crop needs. Our system leverages machine learning (Random Forest Classifier) to predict the ideal fertilizer, thereby improving crop yield and ensuring sustainable farming practices.
📊 Problem Statement
Farmers often lack proper guidance in choosing fertilizers, leading to:
- Nutrient deficiencies in soil
- Reduced crop productivity
- Increased farming costs due to trial-and-error methods
This system aims to simplify fertilizer selection using data-driven insights, empowering farmers with AI support.
🔑 Features
-
Input environmental and soil features:
- Temperature (°C)
- Humidity (%)
- Moisture (%)
- Soil Type (Categorical)
- Crop Type (Categorical)
- Nitrogen (N) value
- Phosphorous (P) value
- Potassium (K) value
-
Predicts the most suitable fertilizer (e.g., Urea, DAP, 28-28, 17-17-17, etc.)
-
Provides a simple and interactive UI using Streamlit
-
Saves trained model using joblib for reusability
🎯 Project Goals
- Build a machine learning model (Random Forest Classifier) for fertilizer prediction
- Develop a Streamlit-based user interface for easy farmer interaction
- Provide accurate and explainable recommendations to aid decision-making
- Contribute to the Agriculture AI section of Social Summer of Code
🛠️ Tech Stack
- Language: Python
- ML Model: Random Forest Classifier
- Libraries: scikit-learn, pandas, joblib
- Frontend/UI: Streamlit
📷 Screenshots (UI Preview)
📌 Relevance to GSSoC’25
- Aligns with Sustainable Agriculture & AI for Social Good
- Provides beginner-friendly exposure to Machine Learning + Streamlit
- Open to contributors for model improvement, UI enhancement, and dataset integration
🚀 New Project Proposal: Fertilizer Recommendation System using Machine Learning 🌾
📌 Project Title
Fertilizer Recommendation System using Machine Learning | Agriculture AI
📝 Description
This project is designed to help farmers and agricultural communities by recommending the most suitable fertilizer based on soil conditions, crop type, and nutrient levels. Many farmers struggle with fertilizer selection due to imbalances in soil nutrients and varying crop needs. Our system leverages machine learning (Random Forest Classifier) to predict the ideal fertilizer, thereby improving crop yield and ensuring sustainable farming practices.
📊 Problem Statement
Farmers often lack proper guidance in choosing fertilizers, leading to:
This system aims to simplify fertilizer selection using data-driven insights, empowering farmers with AI support.
🔑 Features
Input environmental and soil features:
Predicts the most suitable fertilizer (e.g., Urea, DAP, 28-28, 17-17-17, etc.)
Provides a simple and interactive UI using Streamlit
Saves trained model using joblib for reusability
🎯 Project Goals
🛠️ Tech Stack
📷 Screenshots (UI Preview)
📌 Relevance to GSSoC’25