Yield estimation in vineyards by visual grape detection

The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields can allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse - during the growing season sparse samples are taken and extrapolated to determine overall yield. We present an automated method that uses computer vision to detect and count grape berries. The method could potentially be deployed across large vineyards taking measurements at every vine in a non-destructive manner. Our berry detection uses both shape and visual texture and we can demonstrate detection of green berries against a green leaf background. Berry detections are counted and the eventual harvest yield is predicted. Results are presented for 224 vines (over 450 meters) of two different grape varieties and compared against the actual harvest yield as groundtruth. We calibrate our berry count to yield and find that we can predict yield of individual vineyard rows to within 9. 8% of actual crop weight.

Authors

• Stephen T. Nuske
• Supreeth Achar
• Terry Bates
• Srinivasa G. Narasimhan
• Sanjiv Singh

Keywords

• Pipelines
• Agriculture
• Image color analysis
• Image edge detection
• Transforms
• Yield estimation
• Cameras
• Crop Yield
• Yield Prediction
• Grape Varieties
• Grape Berries
• Harvest Yield
• False Positive
• False Negative
• K-nearest Neighbor
• Output Variables
• Focal Length
• Color Development
• Vitis Vinifera
• Image Patches
• Recall Rate
• Image Edge
• Radially Symmetric
• Image Gradient
• Color Discrimination
• Edge Pixels
• Berry Size
• Lack Of Contrast
• Commercial Vineyards