Segmentation of lettuce in coloured 3D point clouds for fresh weight estimation

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  • Anders Krogh Mortensen
  • Asher Bender, Australian Centre for Field Robotics, Sydney University, AustralienBrett Whelan, Sydney Institute of Agriculture, The University of Sydney, AustralienMargaret M. Barbour, Sydney Institute of Agriculture, The University of Sydney, AustralienSalah Sukkarieh, Australian Centre for Field Robotics, Sydney University, Australien
  • Henrik Karstoft
  • René Gislum
Monitoring the health and yield of crops during production is an important, but labour intensive component of commercial agriculture, especially in high value crop such as lettuce. This article proposes a novel method for segmenting lettuce in coloured 3D point clouds and estimating the fresh weight. The proposed segmentation method operates by clustering points into leaves and then evaluating their affiliation to a lettuce of interest. From the segmented lettuce point clouds, the volume, surface area, leaf cover area and height predictors are extracted and correlated to the fresh weight. The proposed segmentation and yield estimation methods are evaluated on Cos and Iceberg lettuce point clouds generated from images collected by an agricultural robot in an outdoor field experiment. The results demonstrate that the proposed segmentation method is able to successfully isolate lettuce (F1-score = 0.88–0.91). Analysis of the segmented lettuce models show that the calculated surface areas correlate strongly with measured fresh weight (R^2 = 0.84–0.94). Not only does this validate the segmentation method, it allows an accurate estimate of the lettuce fresh weight (RMSE = 27–50 g) to be produced non-destructively.
OriginalsprogEngelsk
TidsskriftComputers and Electronics in Agriculture
Vol/bind154
Sider (fra-til)373-381
Antal sider9
ISSN0168-1699
DOI
StatusUdgivet - nov. 2018

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