3D point cloud data to quantitatively characterize size and shape of shrub crops

Yu, Jiguo; Li, Changying; Takeda, Fumiomi; Krämer, Elizabeth; Ashrafi, Hamid; Hunter, J. L.

doi:10.1038/s41438-019-0123-9

Cited by 37 publications

(20 citation statements)

References 48 publications

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“…With the recent advent of high-throughput phenotyping, we envision that more data across different production systems in Florida – and around the globe– could be used for better calibration and ultimately, more accurate predictions. Examples of image analyses for high-throughput phenotyping have been reported in other fruit trees (Diaz-Garcia et al , 2016; Di Gennaro et al , 2019; Koirala et al , 2019; Feldmann et al , 2020), including blueberries (Jiang et al , 2019).…”

Section: Resultsmentioning

confidence: 99%

Genomic prediction in an outcrossing and autotetraploid fruit crop: lessons from blueberry breeding

Ferrão

Amadeu

Benevenuto

et al. 2021

Preprint

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Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop, with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to the expansion of production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on phenotypic recurrent selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberrys breeding cycles are costly and time-consuming, which results in low genetic gains per unit of time. Motivated by the application of molecular markers for a more accurate selection in early stages of breeding, we performed pioneering genomic prediction studies and optimization for implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based genotyping and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic prediction studies and described the current achievements in the crop. In this paper, our contribution for genomic prediction in an autotetraploid crop is three-fold: i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for the use of genomic selection in blueberry, with potential to be applied to other polyploid species of a similar background.

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Section: Resultsmentioning

confidence: 99%

Genomic prediction in an outcrossing and autotetraploid fruit crop: lessons from blueberry breeding

Ferrão

Amadeu

Benevenuto

et al. 2021

Preprint

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“…Although SfM methods were applied to high-spatial-resolution RGB images [129,130] to calculate several strawberry canopy parameters (e.g., canopy area, average height, volume, and smoothness), LiDAR could be used to obtain detailed information about a strawberry plant's structural properties. For example, Jiang et al [166] analyzed LiDAR data and proposed various quantification factors for the bush architecture of blueberries, including bush morphology (height, width, and volume), crown size, and shape descriptors (path curve λ and five shape indices). This type of research can be readily transferred to the strawberry domain.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021

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Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

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“…The 3D point cloud processing is important not only to get physical shapes, but also it can be used to quantitatively characterize the height, width, and volume of shrub plants. Authors in reference [21] analyze point clouds of different blueberry genotype groups to improve the mechanical harvesting process. For comparison of different shrubs, the correlation is used.…”

Section: Related Workmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

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3D point cloud data to quantitatively characterize size and shape of shrub crops

Cited by 37 publications

References 48 publications

Genomic prediction in an outcrossing and autotetraploid fruit crop: lessons from blueberry breeding

Genomic prediction in an outcrossing and autotetraploid fruit crop: lessons from blueberry breeding

Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

Advanced Methods for Point Cloud Processing and Simplification

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