Detection of bird nests during mechanical weeding by incremental background modeling and visual saliency.
Mechanical weeding is an important tool in organic farming. However, the use of mechanical weeding in conventional agriculture is increasing, due to public demands to lower the use of pesticides and an increased number of pesticide-resistant weeds. Ground nesting birds are highly susceptible to farming operations, like mechanical weeding, which may destroy the nests and reduce the survival of chicks and incubating females. This problem has limited focus within agricultural engineering. However, when the number of machines increases, destruction of nests will have an impact on various species. It is therefore necessary to explore and develop new technology in order to avoid these negative ethical consequences. This paper presents a vision-based approach to automated ground nest detection. The algorithm is based on the fusion of visual saliency, which mimics human attention, and incremental background modeling, which enables foreground detection with moving cameras. The algorithm achieves a good detection rate, as it detects 28 of 30 nests at an average distance of 3.8 m, with a true positive rate of 0.75.
Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark. kim.steen@eng.au.dk.
Kim Arild
KA
Steen
Department of Bioscience, Aarhus University, Grenåvej 14, 8410 Rønde, Denmark. oth@dmu.dk.
Ole Roland
OR
Therkildsen
Kongskilde Industries, Strategic Development, Niels Pedersens Allé 2, 8830 Tjele, Denmark. olg@kongskilde.com.
Ole
O
Green
Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark. hka@eng.au.dk.
Henrik
H
Karstoft
D000383:Agriculture; D000818:Animals; D001717:Birds / Q000502:physiology; D005260:Female; D006801:Humans; D058728:Weed Control / Q000379:methods*