Potato AI: Phenotyping and Crop Monitoring

In this livestream, we explore Potato AI: how artificial intelligence, computer vision, RGB photos, multispectral imagery, NDVI, DGCI, SPAD-style greenness indexes, leaf area measurement, and fractional vegetation cover can support better crop monitoring decisions in potatoes and other crops. We start with potato crop growth monitoring, then move into practical examples: how researchers use RGB and multispectral data, why SPAD is useful but limited, how NDVI and DGCI relate to nitrogen and chlorophyll status, and what can be done with a smartphone-based workflow in the field. The second half is a hands-on demo with Petiole Pro, showing leaf area measurement, greenness measurement, calibrating plates, segmentation settings, region-of-interest selection, and fractional vegetation cover for live potato plants. This video is useful for growers, agronomists, plant scientists, crop consultants, researchers, agtech founders, and anyone interested in practical AI in agriculture. ⏱️ Timestamps: 00:00 Welcome to the Potato AI livestream 02:13 What today’s session will cover: potato AI, leaf area, QC and post-harvest 03:12 AI in Agriculture newsletter and why potato became the topic 05:35 Why potato crop monitoring matters during the growing season 06:14 Two research papers on potato monitoring and AI 09:45 How AI can support nitrogen and crop development monitoring 10:02 RGB + multispectral imagery for potato crop health 11:00 SPAD, chlorophyll measurement and greenness indexes 13:20 NDVI explained: what vegetation indexes can show 15:03 Drone maps, sampling points and field-level monitoring 19:51 Vegetation indexes, DGCI and nitrogen status 22:25 RGB photos vs multispectral imagery: what each can measure 24:30 Smartphone-based greenness monitoring and fertilizer decisions 26:46 Fractional vegetation cover and crop development heatmaps 28:19 What fractional vegetation cover means in practice 31:39 Fractional vegetation cover vs leaf area index 35:00 Planning field sampling with smartphones and reference points 40:05 Practical demo begins: potato leaves and leaf preparation 46:42 Calibrating plates for leaf area and greenness measurement 50:00 Measuring potato leaf area in the mobile app 52:00 Segmentation settings: HSV, threshold and measurement correction 54:07 Greenness measurement, ROI size and DGCI values 56:38 Saving measurements and comparing sampling protocols 58:29 Where to find fractional vegetation cover in the app 1:00:00 Measuring fractional vegetation cover on live potato plants 1:03:05 Using a grey card for field batch processing and colour reference 1:06:29 LinkedIn Top Voice, community, comments and smart farming discussions 1:09:31 What’s next: potato quality control and AI agents for potato QC Topics covered: Potato AI and smart crop monitoring AI in agriculture RGB image analysis for plants Multispectral imagery in potato fields NDVI, DGCI and vegetation indexes SPAD and chlorophyll estimation Leaf area measurement Fractional vegetation cover Smartphone-based plant phenotyping Crop sampling protocols Field data collection Potato quality control Petiole Pro mobile app demo Subscribe/follow for more practical conversations about AI in agriculture, computer vision for crops, plant phenotyping, smart farming, drones, field sensing, and digital crop monitoring. #AIinAgriculture #PotatoAI #drmarynakuzmenko