What is the Intelligent Fertilizer Recommendation System?

It is a machine learning-based system that recommends the most suitable fertilizer type based on soil nutrient analysis (N, P, K) and environmental parameters (Temperature, Humidity, Moisture). It implements Random Forest and Logistic Regression with 91.67% accuracy.

Which algorithms are used in this fertilizer recommendation project?

The system implements and compares two algorithms: Random Forest and Logistic Regression. Random Forest achieved superior performance with 91.67% accuracy and 0.917 F1-score.

What is the accuracy of the fertilizer recommendation model?

The Random Forest model achieves 91.67% accuracy with an F1-score of 0.917 and ROC-AUC of 0.987, demonstrating excellent predictive capability for fertilizer recommendation.

What features are used for fertilizer recommendation?

The system uses features including Temperature, Humidity, Moisture, Nitrogen, Potassium, Phosphorous, Soil Type, Crop Type, and engineered features like N_K_Ratio, N_P_Ratio, and Total_NPK.

Who can benefit from this fertilizer recommendation system?

Farmers, agricultural extension officers, agronomists, and agricultural researchers can use this system for data-driven fertilizer decisions, improving crop yield and promoting sustainable agriculture.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and Matplotlib for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

Intelligent Fertilizer Recommendation System - Complete Project Demo Video

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Machine Learning

Intelligent Fertilizer Recommendation System

The Intelligent Fertilizer Recommendation System is a comprehensive machine learning solution that recommends the most suitable fertilizer type based on soil nutrient analysis and environmental parameters. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Random Forest and Logistic Regression with 5-fold cross-validation, achieving 91.67% accuracy and 0.987 ROC-AUC.

The system utilizes a dataset containing soil properties including Nitrogen (N), Phosphorous (P), Potassium (K) levels, along with Temperature, Humidity, and Moisture readings. It provides a user-friendly web interface for data upload, exploratory analysis, model training, and fertilizer prediction, empowering users to make data-driven fertilizer decisions and promote sustainable agricultural practices.

Python 3.8+ Machine Learning Random Forest Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Fertilizer Recommendation
Random Forest (91.67% Accuracy)
Logistic Regression (87.78% Accuracy)
Feature Engineering (N_K_Ratio, Total_NPK)
7 Fertilizer Types Supported
Interactive Visualizations
Real-time Fertilizer Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with multi-class OVR, max_iter=1000, solver='lbfgs'

🎯 Accuracy: 87.78%

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=10, class_weight='balanced'

🎯 Accuracy: 91.67%

🔧 Feature Engineering

N_K_Ratio, N_P_Ratio, K_P_Ratio, Total_NPK, Temp_Humidity_Ratio

📊 Top Feature: Total_NPK

📊 Cross-Validation

Stratified 5-fold CV with leak-free evaluation

📊 CV Mean: 89.38%

Methodology & Workflow

1 Data Collection
400+ soil samples with NPK and environmental features

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Random Forest and Logistic Regression with Stratified 5-fold CV

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time fertilizer prediction

Model Performance Comparison

Metric	Logistic Regression	Random Forest	Best
Test Accuracy	87.78%	91.67%	Random Forest
Test Precision	88.33%	92.08%	Random Forest
Test Recall	87.78%	91.67%	Random Forest
Test F1-Score	87.56%	91.69%	Random Forest
Test ROC-AUC	96.33%	98.71%	Random Forest
CV Mean Accuracy	85.94%	89.38%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Fertilizer Prediction Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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