Overview:
The Smart Farming: Crop Recommendation System is an AI-driven agricultural decision-support tool that utilizes a Random Forest Classifier to recommend the most suitable crops for cultivation. By leveraging multi-dimensional environmental parameters including Nitrogen (N), Phosphorus (P), Potassium (K), soil pH, humidity, temperature, and rainfall, the system achieves an impressive 94.3% accuracy in crop prediction. The model is trained on curated datasets of Indian agricultural conditions and is optimized for practical, real-world application.
Developed as part of an academic research project at IIIT Lucknow, this system aims to enhance sustainable farming practices, improve yield optimization, and support data-driven decision-making for Indian farmers.
Key Features:
Developer Spotlight:
Project Lead | AI/ML Engineer | Full-Stack Developer
Saksham led the end-to-end development of the system — from data preprocessing and feature engineering to training the Random Forest model and designing the UI/UX. He integrated the backend using Flask, deployed the project on Hugging Face Spaces using Docker, and authored the associated academic research paper detailing the methodology, experimentation, and results. His focus was on building a scalable, accurate, and user-centric AI solution tailored to Indian agricultural conditions.