AI Powered Hyper Spectral Seed Analysis Device

In modern agriculture, ensuring high seed quality is essential for maximizing crop yield, maintaining food security, and reducing post-harvest losses. Traditional seed quality assessment methods are largely manual, time- consuming, and prone to human error. This project presents the design and development of an AI Powered Hyperspectral Seed Analysis Device that enables automated, real-time classification and grading of seeds using computer vision, deep learning, and gas-sensing technologies. The system integrates a camera-based imaging module with MQ-3 and MQ-6 gas sensors connected to a microcontroller. Seed images are processed using a YOLO-based deep learning model to detect defects and classify seeds into quality grades, while the gas sensors monitor chemical emissions to identify spoilage or contamination. The combined visual and environmental analysis provides a non- destructive, scalable, and accurate method for seed quality evaluation. The proposed solution is cost-effective, portable, and suitable for applications in agriculture, seed production, and quality certification industries.