This project aims to develop a cost-effective, innovative near-infrared (NIR) measurement setup using integrated NIR and near-infrared transmittance (NIT) spectral sensors for non-destructive, single-seed biochemical analysis, addressing the drawbacks of destructive, time-consuming, and environmentally harmful traditional methods. Over 20 months, 12 researchers and 4 fellows from Turkey and Austria, in collaboration with 2 small and medium-sized enterprises (SMEs), 1 end-user company, and 2 universities (as subcontractor), will design and build a prototype integrating advanced spectroscopy, automation, robotics, and AI-based modeling. The system will use machine learning (XGBoost), deep learning (TabTransformer) algorithms, alongside classical partial least squares regression (PLSR) to analyze major (moisture, protein, oil, carbohydrates) and minor (fatty acids, essential amino acids) biochemical compounds in maize seeds. The project consists of four work packages (WPs): WP1–System Design (2 months), WP2–Development of Functional Parts (8 months), WP3–Model Development and AI Integration (6 months), and WP4–Final Prototyping and Testing (4 months). The proposed system has significant potential for patenting, commercialization, and broader application to various plant seeds beyond maize, offering a transformative tool for agricultural research and plant breeding.