Detection of bird nests during mechanical weeding by incremental background modeling and visual saliency

Sensors
Kim Arild SteenHenrik Karstoft

Abstract

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.

References

Nov 14, 2006·Neural Networks : the Official Journal of the International Neural Network Society·Dirk Walther, Christof Koch
Jan 5, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Tie LiuHeung-Yeung Shum
Sep 10, 2014·Sensors·Peter ChristiansenHenrik Karstoft

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Citations

Mar 15, 2016·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Jin-Gang YuJinwen Tian

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MATLAB

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