I'm excited to share AgriMind – Smart Crop Recommendation System, a machine learning project built to strengthen my understanding of supervised learning by taking a problem from raw data all the way to a deployed web application.
Rather than focusing only on building a prediction model, my goal was to understand the complete machine learning workflow: data analysis, preprocessing, model selection, evaluation, deployment, and real-world usage.
For this project, I trained and evaluated multiple supervised learning algorithms on the same agricultural dataset, including Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, and Random Forest. Each model was tested and compared using the same training and evaluation process to identify the best-performing approach.
After comparing the results, Random Forest delivered the most reliable performance. By combining multiple decision trees, it reduced overfitting and produced more stable predictions. Since crop recommendation depends on factors such as NPK values, temperature, humidity, pH, and rainfall, the ensemble approach captured these relationships more effectively than the other models tested.
The final model recommends suitable crops based on:
• Nitrogen (N)
• Phosphorus (P)
• Potassium (K)
• Temperature
• Humidity
• pH Value
• Rainfall
Supported Crops
Rice, Maize, Chickpea, Kidney Beans, Pigeon Peas, Mung Bean, Black Gram, Lentil, Pomegranate, Banana, Mango, Grapes, Watermelon, Apple, Orange, Papaya, Coconut, Cotton, Jute, and Coffee.
Tech Stack
Machine Learning: Python, Scikit-Learn, Pandas, NumPy, Joblib
Backend: FastAPI
Frontend: HTML, CSS, JavaScript
Deployment: Render
AgriMind was trained using publicly available open-source agricultural datasets. Recommendations are highly dependent on the input values provided, especially NPK values. Predictions should be considered data-driven recommendations rather than absolute decisions.
This project helped me strengthen concepts such as data preprocessing, train-test splitting, model evaluation, overfitting vs generalization, ensemble learning, API integration, and ML deployment.
Live Demo: https://lnkd.in/dpGXCqbr
GitHub Repository: https://lnkd.in/dBB327WF
I would appreciate any feedback or suggestions from the community.
#MachineLearning #ArtificialIntelligence #DataScience #ScikitLearn #RandomForest #FastAPI #WebDevelopment #AgriTech #MLProject
13 August 2026 à 06h27
View on LinkedIn →
