Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN

Research output: Contribution to conferencePaperAcademic

Abstract

In this paper we present the method and performance to detect tomato whitefly and its predatory bugs on yellow sticky traps. These traps are imaged in controlled light conditions with a digital single lens reflex camera and in uncontrolled environment with smartphone camera. The method consists of the following steps. First, image sub setting and data labelling by manual annotation. Secondly, training a deep learning convolutional neural network. Third step is classification of the images. Final step is comparison with hand counted data of insects. The weighted averaged accuracy for deep learning detected insects was 87.4%. The correlation of hand counted insects with deep learning counted insects was over 0.95 for the smartphone images. The methods used show that the training data used on controlledconditions could be transferred to uncontrolled smartphone imaging conditions for the data provide
Original languageEnglish
Number of pages4
Publication statusPublished - 26 Sep 2018
EventThe Netherlands Conference on Computer Vision - Eindhoven, Netherlands
Duration: 26 Sep 201827 Sep 2018
http://nccv18.nl/program/

Conference

ConferenceThe Netherlands Conference on Computer Vision
Abbreviated titleNCCV18
CountryNetherlands
CityEindhoven
Period26/09/1827/09/18
Internet address

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Smartphones
Crops
Camera lenses
Labeling
Cameras
Neural networks
Imaging techniques
Deep learning

Cite this

Nieuwenhuizen, A. T., Hemming, J., & Suh, H. K. (2018). Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. Paper presented at The Netherlands Conference on Computer Vision, Eindhoven, Netherlands.
Nieuwenhuizen, A.T. ; Hemming, J. ; Suh, H.K. / Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. Paper presented at The Netherlands Conference on Computer Vision, Eindhoven, Netherlands.4 p.
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title = "Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN",
abstract = "In this paper we present the method and performance to detect tomato whitefly and its predatory bugs on yellow sticky traps. These traps are imaged in controlled light conditions with a digital single lens reflex camera and in uncontrolled environment with smartphone camera. The method consists of the following steps. First, image sub setting and data labelling by manual annotation. Secondly, training a deep learning convolutional neural network. Third step is classification of the images. Final step is comparison with hand counted data of insects. The weighted averaged accuracy for deep learning detected insects was 87.4{\%}. The correlation of hand counted insects with deep learning counted insects was over 0.95 for the smartphone images. The methods used show that the training data used on controlledconditions could be transferred to uncontrolled smartphone imaging conditions for the data provide",
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note = "The Netherlands Conference on Computer Vision, NCCV18 ; Conference date: 26-09-2018 Through 27-09-2018",
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Nieuwenhuizen, AT, Hemming, J & Suh, HK 2018, 'Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN' Paper presented at The Netherlands Conference on Computer Vision, Eindhoven, Netherlands, 26/09/18 - 27/09/18, .

Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. / Nieuwenhuizen, A.T.; Hemming, J.; Suh, H.K.

2018. Paper presented at The Netherlands Conference on Computer Vision, Eindhoven, Netherlands.

Research output: Contribution to conferencePaperAcademic

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AB - In this paper we present the method and performance to detect tomato whitefly and its predatory bugs on yellow sticky traps. These traps are imaged in controlled light conditions with a digital single lens reflex camera and in uncontrolled environment with smartphone camera. The method consists of the following steps. First, image sub setting and data labelling by manual annotation. Secondly, training a deep learning convolutional neural network. Third step is classification of the images. Final step is comparison with hand counted data of insects. The weighted averaged accuracy for deep learning detected insects was 87.4%. The correlation of hand counted insects with deep learning counted insects was over 0.95 for the smartphone images. The methods used show that the training data used on controlledconditions could be transferred to uncontrolled smartphone imaging conditions for the data provide

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Nieuwenhuizen AT, Hemming J, Suh HK. Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. 2018. Paper presented at The Netherlands Conference on Computer Vision, Eindhoven, Netherlands.