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Bird Detection, Tracking and Counting in Wine Grapes

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2017 ASABE Annual International Meeting  1700300.(doi:10.13031/aim.201700300)
Authors:   Santosh Bhusal, Shivam Goel, Kapil Khanal, Matthew Taylor, Manoj Karkee
Keywords:   Bird Detection, Bird Deterrence, Bird Tracking, Bird Counting, Vineyards

Abstract.

Bird damage in fruit crops is a critical problem in wine grapes, blueberries and other fruit crops especially during the weeks close to the harvesting period. Usually small birds such as Starlings, Robins and Finches feed extensively on wine grapes. Automated detection, localization, and tracking of these birds in the field will be necessary to identify best locations for installing bird scaring devices in the field as well as to use autonomous UAS operation to deter them. A section of wine grape plot (~30 m x 30 m) was constantly monitored using four GoPro cameras installed at the four corners of the plot. Videos were recorded at 1080p resolution with 30 frames per second. In this paper, Gaussian mixture-based Background/Foreground Segmentation Algorithm was used in detecting birds flying in and out of the wine grape plot. This algorithm can detect moving objects in a video irrespective of their shape, size and color. Detected birds were tracked over a period of time using Kalman filter. Then, a field boundary was defined to estimate the count of the birds flying in and out of the plot through the boundary. Two performance measures, precision and recall (sensitivity), were used to analyze the accuracy of the counting method. Precision refers to the usefulness of the system and recall measures its completeness. Results showed that the proposed method can achieve a precision of 85% in counting birds entering or leaving a crop field with a sensitivity of 87%. Such a system could have a wide range of applications when birds‘ presence is a problem such as in crop fields, airport and cattle farms.

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