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Agritech-AI
Python Scikit-learn Flask Next.js Machine Learning
An end-to-end Machine Learning pipeline for data-driven crop recommendation and agricultural insights.
The Challenge
Local farmers often lack access to data-driven insights to make informed decisions about crop selection based on soil composition and climate data.
The Engineering
Developed an end-to-end ML solution. The backend uses Python (Scikit-learn, Pandas, Flask) to process datasets and serve a trained crop recommendation model. The frontend, built with Next.js, provides a high-performance interface for users to input real-time environmental data and receive actionable agricultural predictions.