Intelligent Robot Learning Laboratory (IRL Lab) Autonomous UAV for Bird Deterrance/Avoidance from the Cherry Orchards

By: Shivam Goel and Matthew E. Taylor

Cherry, grape, honeycrisp apple, and blueberry growers lose $80 million annually to bird damage in the state of Washington (WA) alone. Over 50% of sweet cherry growers in Washington consider bird damage as one of the significant factors affecting profit. Netting, auditory scare devices, visual scare devices, chemical applications, and active methods such as trapping, falconry, and lethal shooting are the most common ways bird damage is minimized currently. Netting and falconry are the only methods viewed by most growers as effective, but are costly, and netting is potentially lethal to a host of non-target wildlife. Growers of a wide range of crops have a critical need for a safe, cost effective method for persistent bird deterrence, resulting in reduced costs and increased sustainability.

Unmanned Aerial Systems (UAS) have recently grown in popularity in military, civilian, and agriculture domains due to their decreasing cost, maneuverability, and ability to tackle multiple types of missions. WA is expected to become a leader in the development and manufacture of UAS, with anticipated $1.3 billion in economic activity and 6,746 jobs by 2017. It is expected that agriculture will see more than 80% of the total growth in UAS-related economic activities.

Therefore, UAS and its application in agriculture is a critically important emerging area for WSU to lead the effort in the nation. Lack of such research activities at WSU is a critical drawback and is limiting our ability to bring WSU to the forefront of this emerging issue. We strongly believe that the seed funding in this area will help us be highly competitive in securing extramural funding and develop highly beneficial agricultural applications of UAS for WA growers.

The long-term objective of the proposed research is to develop an autonomous UAS for bird deterrence, as well as fully test its effectiveness and financial implications. In addition to bird deterrence in high-value fruit crops, such a system would also be able to deter birds in a variety of other situations, such as at landfills, dairy farms, and airports. [1, 2]

[1] [pdf] [doi] Shivam Goel, Santosh Bhusal, Matthew E. Taylor, and Manoj Karkee. Detection and localization of birds for Bird Deterrence using UAS. In 2017 ASABE Annual International Meeting, page 1. American Society of Agricultural and Biological Engineers,, 2017. This is not a peer-reviewed article
[Bibtex]
@inproceedings{2017ASABE-Goel,
author={Goel, Shivam and Bhusal, Santosh and Taylor, Matthew E and Karkee, Manoj},
title={{Detection and localization of birds for Bird Deterrence using UAS}},
booktitle={{2017 {ASABE} Annual International Meeting}},
pages={1},
year={2017},
doi={10.13031/aim.201701288},
note={This is not a peer-reviewed article},
organization={American Society of Agricultural and Biological Engineers},
abstract={Cherry, grape, and blueberry growers lose around 80 million dollars annually to bird damage in the state of Washington alone. Growers of a wide range of crops have a critical need for a safe and cost-effective method for persistent bird deterrence, which would lead to significantly reduced production costs. The goal of this research is to build a completely autonomous Unmanned Aerial System (UAS) to deter birds from the blueberry fields and grape vineyards. In the effort to build the UAS, the most vital part of its implementation is the vision system. The primary objective of this paper is to build a system to detect and localize birds. To detect birds, background subtraction algorithms have been used and the performance of various background subtraction algorithms are measured. It is found out that ViBe, a background subtraction algorithm, performs best in the bird detection scenario and provides an accuracy of 63%. In the quest of improving the bird detection speed and obtaining it in real time, a split window technique is used to improve the detection speed by 13%. To estimate the distance of the detected bird, a stereo vision system is proposed. With our current system, an accurate measure of the distance of the object is possible from 2 to 7 meters with an error accuracy of 30 centimeters. The long-term goal is to combine the efforts of the paper to successfully create a completely autonomous Smart Scarecrow that can safely, effectively and reliably scare and deter birds from high-value crops.}
}
[2] [pdf] [doi] Santosh Bhusal, Shivam Goel, Kapil Khanal, Matthew E. Taylor, and Manoj Karkee. Bird detection, tracking and counting in wine grapes. In 2017 ASABE annual international meeting, page 1. American Society of Agricultural and Biological Engineers,, 2017. This is not a peer-reviewed article
[Bibtex]
@inproceedings{2017ASABE-Bhusal,
title={Bird Detection, Tracking and Counting in Wine Grapes},
author={Bhusal, Santosh and Goel, Shivam and Khanal, Kapil and Taylor, Matthew E. and Karkee, Manoj},
booktitle={2017 {ASABE} Annual International Meeting},
pages={1},
year={2017},
doi={10.13031/aim.201700300},
note={This is not a peer-reviewed article},
organization={American Society of Agricultural and Biological Engineers},
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.}
}