Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12899 LNCS, Page: 223-233
2021
- 6Citations
- 8Captures
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Conference Paper Description
Manual assessment of soft-fruits is both laborious and prone to human error. We present methods to compute width, height, cross-section length, volume and mass using computer vision cameras from a robotic platform. Estimation of phenotypic traits from a camera system on a mobile robot is a non-destructive/invasive approach to gathering quantitative fruit data which is critical for breeding programmes, in-field quality assessment, maturity estimation and yield forecasting. Our presented methods can process 324–1770 berries per second on consumer grade hardware and achieve low error rates of 3.00 cm and 2.34 g for volume and mass estimates. Our methods require object masks from 2D images, a typical output of segmentation architectures such as Mask R-CNN, and depth data for computing scale.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85115860261&origin=inward; http://dx.doi.org/10.1007/978-3-030-87156-7_18; https://link.springer.com/10.1007/978-3-030-87156-7_18; https://link.springer.com/content/pdf/10.1007/978-3-030-87156-7_18; https://dx.doi.org/10.1007/978-3-030-87156-7_18; https://link.springer.com/chapter/10.1007/978-3-030-87156-7_18
Springer Science and Business Media LLC
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