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Size estimation of potato tubers in different clustering scenarios using Mask R-CNN and image processing techniques
Authors: Ighodaro Kelvin Emwinghare,
Ahmad Al-Mallahi
Identifier: CSBE23173
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Published in: CSBE-SCGAB Technical Conferences » AGM Lethbridge 2023
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Description: This study presents a method for size estimation of potato tubers under different clustering conditions. Images of potato tubers on a conveyor at lab in Dalhousie University, Department of Engineering, were captured using a commercially available RGB camera mounted above the conveyor. The captured images were labelled and fed to a Mask R-CNN (Region-based Convolutional Neural Network) deep learning architecture, which enabled instance segmentation of potato tubers even in cases of severe clustering (i.e., more than 100 tubers per frame) and mutually occluding tubers. Given that not all tubers were fully visible, image processing techniques were employed to randomly choose five fully visible potato tubers per frame. The minor and major diameters of the chosen tubers were calculated by fitting an ellipse over the detected potato mask and outputting the lengths of the minor and major axes. Our method was demonstrated by using six images for each of the following clustering conditions: dense (100 - 200 tubers per frame), moderate (50 - 100 tubers per frame), and sparse (1 - 50 tubers per frame). The coefficient of determination for the major and minor diameters was 0.94 and 0.78 for all clustering scenarios, respectively, with the dense scenario having coefficients of 0.89 and 0.76. This technique allows for measuring minor and major diameters under varying clustering conditions without manual sampling and could replace the traditional size grading methods.
Keywords: Deep learning, Machine vision, Image processing, Precision agriculture
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Date: 2023-07-23
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Conference name: CSBE/SCGAB 2023 Annual Conference, Lethbridge, Alberta, 23-26 July 2023.
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Type: Presentation
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Publication type: Text.Abstract
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Coverage: North_America
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
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DOI:
Description: This study presents a method for size estimation of potato tubers under different clustering conditions. Images of potato tubers on a conveyor at lab in Dalhousie University, Department of Engineering, were captured using a commercially available RGB camera mounted above the conveyor. The captured images were labelled and fed to a Mask R-CNN (Region-based Convolutional Neural Network) deep learning architecture, which enabled instance segmentation of potato tubers even in cases of severe clustering (i.e., more than 100 tubers per frame) and mutually occluding tubers. Given that not all tubers were fully visible, image processing techniques were employed to randomly choose five fully visible potato tubers per frame. The minor and major diameters of the chosen tubers were calculated by fitting an ellipse over the detected potato mask and outputting the lengths of the minor and major axes. Our method was demonstrated by using six images for each of the following clustering conditions: dense (100 - 200 tubers per frame), moderate (50 - 100 tubers per frame), and sparse (1 - 50 tubers per frame). The coefficient of determination for the major and minor diameters was 0.94 and 0.78 for all clustering scenarios, respectively, with the dense scenario having coefficients of 0.89 and 0.76. This technique allows for measuring minor and major diameters under varying clustering conditions without manual sampling and could replace the traditional size grading methods.
Keywords: Deep learning, Machine vision, Image processing, Precision agriculture
Résumé:
Mots-clés:
Citation:
Volume:
Issue:
Pages -
Contributor:
Date: 2023-07-23
Technical field:
Conference name: CSBE/SCGAB 2023 Annual Conference, Lethbridge, Alberta, 23-26 July 2023.
Session name:
Other information:
Type: Presentation
Format:
Publication type: Text.Abstract
Source:
Relation:
Coverage: North_America
Language 1: en
Language 2:
Rights: Canadian Society for Bioengineering
Notes:
Other files:
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