Crop & Fertilizer Recommendation System.

Problem Statement

Farmers often rely on traditional knowledge or guesswork to decide which crop to plant and which fertilizer to use. This leads to reduced yield, inefficient fertilizer use, and higher costs. The challenge was to build a data-driven decision support system that could guide farmers with accurate predictions in real time.

Technical Solution

• Step 1 – Architecture & Approach: Built a Flask-based web application where soil and weather data are collected as inputs. The data flows through preprocessing (feature scaling, handling missing values) before being fed into a Random Forest Classifier. The model predicts the optimal crop type and suggests a matching fertilizer.
• Step 2 – Algorithms, Libraries & Highlights: • Machine Learning: Random Forest Classifier for multi-class classification of crops. • Libraries: Pandas (data handling), scikit-learn (model building & evaluation), Numpy (math operations). • Interface: Tkinter GUI for desktop version and Flask web backend for online deployment. • Highlights: Achieved high prediction accuracy with minimal overfitting by using ensemble learning.
• Step 3 – Deployment & Infrastructure: • Backend hosted on Render Cloud. • Packaged ML model integrated into Flask routes for real-time predictions. • Future scalability planned with AWS SageMaker for large-scale deployment and mobile app integration.

Results & Impact

Enabled data-driven crop selection, reducing dependency on guesswork.

Technologies Used