TY - GEN
T1 - Developing an automatic system aiming to detect and deter migrating birds from aquaculture ponds
AU - Geffen, O.
AU - Yitzhaky, Y.
AU - Glassner, H.
AU - Katzir, G.
AU - Halachmi, I.
N1 - Publisher Copyright:
© Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Israel is a major global migration route for many species of fish-eating birds, such as pelicans and cormorants. This migration carries high costs for aquaculture in fish predation and the spread of disease in the fish ponds. The aim of this study was to develop a computerised automatic system to detect and deter fish-eating birds from fish ponds. We developed a system that detects pelicans and sends a small boat to deter them. The system comprises a radio-controlled boat (1 m long), a stationary video camera, a computer, and a detection algorithm. The camera (Dahua camera, DH-IPC-HFW4830E-S) was connected to the computer via an Ethernet cable to collect video sequences of a focal pond. The detection algorithm is based on a convolutional neural network, and classifies different regions as containing pelicans, or not. Data were recorded over the three months before the migration season. The data contained videos of between two and 50 pelicans per video. The size of the test fish pond was 105 m2 with a maximum range of 600 m to the pelicans as they floated. The detection complexity varies according to air clarity, cloud cover, and target proximity. Pelicans were detected on clear days, cloudy days, and with different distances to the camera. The detection accuracy was 98%. The accuracy was calculated based on a dataset that was built for this project. The system is currently being tested and is on the way to become fully automatic.
AB - Israel is a major global migration route for many species of fish-eating birds, such as pelicans and cormorants. This migration carries high costs for aquaculture in fish predation and the spread of disease in the fish ponds. The aim of this study was to develop a computerised automatic system to detect and deter fish-eating birds from fish ponds. We developed a system that detects pelicans and sends a small boat to deter them. The system comprises a radio-controlled boat (1 m long), a stationary video camera, a computer, and a detection algorithm. The camera (Dahua camera, DH-IPC-HFW4830E-S) was connected to the computer via an Ethernet cable to collect video sequences of a focal pond. The detection algorithm is based on a convolutional neural network, and classifies different regions as containing pelicans, or not. Data were recorded over the three months before the migration season. The data contained videos of between two and 50 pelicans per video. The size of the test fish pond was 105 m2 with a maximum range of 600 m to the pelicans as they floated. The detection complexity varies according to air clarity, cloud cover, and target proximity. Pelicans were detected on clear days, cloudy days, and with different distances to the camera. The detection accuracy was 98%. The accuracy was calculated based on a dataset that was built for this project. The system is currently being tested and is on the way to become fully automatic.
KW - Automated birds deterrence
KW - Birds detection
KW - Birds deterrence
KW - Deep learning
KW - Fish farming
KW - ResNet
UR - http://www.scopus.com/inward/record.url?scp=85073753350&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073753350
T3 - Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
SP - 899
EP - 902
BT - Precision Livestock Farming 2019 - Papers Presented at the 9th European Conference on Precision Livestock Farming, ECPLF 2019
A2 - O'Brien, Bernadette
A2 - Hennessy, Deirdre
A2 - Shalloo, Laurence
PB - Organising Committee of the 9th European Conference on Precision Livestock Farming (ECPLF), Teagasc, Animal and Grassland Research and Innovation Centre
T2 - 9th European Conference on Precision Livestock Farming, ECPLF 2019
Y2 - 26 August 2019 through 29 August 2019
ER -