AI Greenhouse Monitoring for Cannabis/Hemp: Raspberry Pi Cameras, Sensors & YOLO Explained скачать в хорошем качестве

AI Greenhouse Monitoring for Cannabis/Hemp: Raspberry Pi Cameras, Sensors & YOLO Explained

Ready to turn a greenhouse into a smart, self-auditing space? In this video, we build an affordable, open-source monitoring system for hemp/cannabis using Raspberry Pi cameras, off-the-shelf sensors, and on-device AI (YOLO). You’ll see the full journey: hardware setup, data capture, labelling, training, deployment, dashboards, and real-world alerts—so you can replicate it in your own tunnel or glasshouse. Timestamps 00:00 Intro — AI in Agriculture: One Idea a Day 00:18 Topic setup — hemp vs. cannabis clarification 00:44 Newsletter note — today’s 3 research papers 01:05 Focus paper — greenhouse monitoring overview 01:28 Hardware used — 3 USB cams + Raspberry Pi 01:56 Sensor suite — temp, humidity, CO₂; routine photos 02:24 Plot design — 3 cams × 4 plants = 12 plants tracked 02:52 Imaging cadence — how and when frames were captured 03:18 YOLO on-device — what the model detects well 03:48 Strengths of the approach — why this matters 04:20 Weak points — lighting consistency & drift 04:52 Calibration plates — colour/scale reference explained 05:26 Green index example — reading canopy colour change 05:58 Fixed camera limits — 3D plant structure challenges 06:34 Cost reality — Pi (~£100) vs. pricier cameras 07:10 Edge processing — no monthly fees, private by default 07:44 Data flow — local processing, optional cloud export 08:16 Niche know-how — why domain specifics matter 08:44 Practical takeaways — what you can standardise today 09:14 What we’d improve — active learning, better lighting 09:42 Q&A invite — ask in comments 10:02 Wrap-up — next episodes & thanks What you’ll learn 1. How to design a low-cost monitoring stack with Raspberry Pi + CSI/USB cameras 2. Which sensors matter (temp/RH, CO₂, light/PAR, soil moisture) and where to place them 3. A practical imaging protocol for consistent, usable photos (lighting, angle, background, scale) 4. Fast labelling tips and a lean dataset strategy for early results 5. Training YOLO for leaf health, pest signs, and growth-stage cues 6. Running inference on-device and sending results to the cloud (or staying fully offline) 7. Building a single dashboard for everything (images, metrics, AI detections) 8. When to alert (thresholds vs. trends) and how to avoid alarm fatigue 9. ROI math: fewer scouting laps, earlier stress detection, and better batch uniformity Hardware & software used Raspberry Pi (4/5) + official camera (or USB industrial cam) Sensors: BME680/DHT22 (temp/RH), NDIR CO₂, PAR/illuminance, capacitive soil probes Mounts: adjustable arm + light diffuser for consistent frames Storage: local SSD or NAS Software: Python, OpenCV, YOLO, MQTT/Node-RED, InfluxDB, Grafana (or your favourite stack) Imaging best practices (quick checklist) Fix distance & angle (mark floor/bench positions) Use diffuse light; avoid harsh shadows and rolling flicker Include a small reference card (scale & colour) in the frame Capture at regular intervals (e.g., every 30 minutes) + one daily “inspection set” Name files with time and location to simplify training later Dataset & model tips Start ~300–500 labelled images across 3–5 classes you truly care about Targeted augmentations (flip, slight rotation, mild brightness/contrast) Validate across days and beds to catch real variability Track precision/recall per class; promote “hard examples” into retraining Retrain monthly or after major environment changes Edge deployment & alerts Quantise (INT8/FP16) for faster Pi inference Batch detections; emit deltas or daily summaries to avoid spam Trend-aware alerts: e.g., VPD off-range ≥20 min + mildew sign detected Keep a human-in-the-loop with a short daily review Dashboards that matter Today at a glance: VPD, CO₂, temp/RH, light, irrigation events AI panel: latest detections with thumbnails + confidence Trends: 7-day plots with annotations QC checklist: images captured? sensors healthy? model up to date? Who is this for? Growers, agronomists, and R&D teams who need trustworthy, repeatable data without enterprise price tags. Also perfect for makers/students building real-world agtech. Notes & responsibility Always follow local laws and licensing for hemp/cannabis cultivation and data collection. Educational content only. #drmarynakuzmenko #cannabiseducation #aiinagriculture DR. MARYNA KUZMENKO 1. Linkedin:   / kuzmenkomaryna   2. Instagram:   / dr.maryna.kuzmenko   3. TikTok:   / dr.maryna.kuzmenko   4. X: https://x.com/mary_kuzmenko 5. Facebook:   / dr.maryna.kuzmenko   ___________________________ 🎁👉 FREE online beginner-level course "AI in Agriculture: Practical Introductory Course" on Udemy: https://www.udemy.com/course/ai-in-ag...