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In this video, we show how computer vision can detect, classify and predict seed quality/viability before germination using RGB, X-ray and hyperspectral (HSI) images—explained simply and with practical use-cases for growers and seed banks. 🔍 What you’ll see in 2 minutes How a high-res photo lets AI detect each seed and classify external traits (size, shape, mottling). Why X-ray reveals internal defects (voids, embryo damage) without cracking the seed. What HSI measures and how spectral “fingerprints” help estimate vigor/viability. A quick look at auto-labelling and Faster R-CNN/RetinaNet pipelines to speed up QC. 🧠 How it works (plain English) Capture seed images (RGB; X-ray/HSI in lab settings). The software boxes each seed (detection). A model classifies variety/defects and predicts whether it’s worth planting. You export a “winning” batch and cut losses. ✅ Benefits Non-destructive assessment. Fast & scalable for large batches. Consistent decisions across lots. ⚠️ Real-world constraints Lighting/camera shifts reduce accuracy—standardise the setup. Needs larger datasets and calibration for new chemovars. HSI/X-ray may require specialised equipment and workflow. 🏭 Where it fits Seed banks and pro producers needing high-throughput QC. Commercial grows seeking lot-level traceability. R&D in breeding and quality control. 🧩 Takeaway AI doesn’t change genetics—it prioritises smartly. It helps you choose better, reduce waste, and speed up decisions. If you enjoy this, like the video, subscribe and tap the bell. More applied science episodes coming soon! #cannabiseducation #amsterdammarijuanaseeds #amsterdam #cannabis #cannabisbenefits