2020
DOI: 10.1007/978-3-030-65414-6_18
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3D Plant Phenotyping: All You Need is Labelled Point Cloud Data

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Cited by 11 publications
(12 citation statements)
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“…There exists many platforms for constructing virtual plant models such as L+C modelling language [40,41] and L-Py framework [42], which are based on the formalism of L-systems [24]. Despite the availability of such platforms capable of generating synthetic plants with complex architectures, employing them as 3D training data in the form of point clouds for plant phenotyping is only considered very recently [23].…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…There exists many platforms for constructing virtual plant models such as L+C modelling language [40,41] and L-Py framework [42], which are based on the formalism of L-systems [24]. Despite the availability of such platforms capable of generating synthetic plants with complex architectures, employing them as 3D training data in the form of point clouds for plant phenotyping is only considered very recently [23].…”
Section: Related Workmentioning
confidence: 99%
“…The main factor that impedes the 3D deep learning techniques from being applied to plant phenotyping is the lack of large annotated 3D plant data sets [23]. Even moderate size annotated data sets of full plant models are not available.…”
Section: Arxiv:201211489v1 [Cscv] 21 Dec 2020mentioning
confidence: 99%
See 1 more Smart Citation
“…Fruit appearance is a key trait for many crops and can condi-2 tion market viability of fruit products and the success of cul-successfully implemented to measure the shape and size of fruits such as strawberries (4, 12), apples (5), carrot (6, 14), mangoes (28), and many others. More recently, methodologies for 3D reconstruction of plant organs have been developed with approaches that vary in speed, scale, cost, and accuracy; including laser scanners, x-ray computed tomography, and reconstruction from sequences of 2D images from digital cameras (8, [29][30][31][32][33][34][35][36][37][38][39][40][41]. Methods that rely on sequences of 2D images are numerous and variable with their own complexities and nuances that provide different strengths and weaknesses (8,27,37,(40)(41)(42)(43)(44).…”
Section: Introductionmentioning
confidence: 99%
“…Computer vision has shown great potential to quantify external fruit quality and 2D imaging has been successfully implemented to measure the shape and size of fruits such as strawberries (Ishikawa et al, 2018; Feldmann et al, 2020), apples (Migicovsky et al, 2016a), carrot (Horgan, 2001; Turner et al, 2018), mangoes (Naik et al, 2015), and many others. More recently, methodologies for 3D reconstruction of plant organs have been developed with approaches that vary in speed, scale, cost, and accuracy; including laser scanners, x-ray computed tomography, and reconstruction from sequences of 2D images from digital cameras (Gaillard et al, 2020; Chaudhury et al, 2020; Feldman et al, 2021; Dutagaci et al, 2020; Artzet et al, 2020; Teramoto et al, 2020; He et al, 2017; Paulus et al, 2014; Li et al, 2014; Liu et al, 2020; Dowd et al, 2021; Jiang et al, 2019; Sandhu et al, 2019; Hu et al, 2020). Methods that rely on sequences of 2D images are numerous and variable with their own complexities and nuances that provide different strengths and weaknesses (He et al, 2017; Porter et al, 2016; Sandhu et al, 2019; Hu et al, 2020; Wang et al, 2019; Liu et al, 2017, 2020; Warman et al, 2021).…”
Section: Introductionmentioning
confidence: 99%