ArticleFrontiers in plant science2022
A non-destructive coconut fruit and seed traits extraction method based on Micro-CT and deeplabV3+ model.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A Multidimensional Approach to Cereal Caryopsis Development: Insights into Adlay (Plants (Basel, Switzerland) · 2026Article
- CitrusGAN: sparse-view X-ray CT reconstruction for citrus based on generative adversarial networks.Plant phenomics (Washington, D.C.) · 2025Article
- XFruitSeg-A general plant fruit segmentation model based on CT imaging.Plant phenomics (Washington, D.C.) · 2025Article
- CT image segmentation of foxtail millet seeds based on semantic segmentation model VGG16-UNet.Plant methods · 2024Article
- Article
- Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging.Frontiers in plant science · 2024Article
- Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology.Frontiers in plant science · 2023Article
- An improved Deeplab V3+ network based coconut CT image segmentation method.Frontiers in plant science · 2023Article
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Abstract
With the completion of the coconut gene map and the gradual improvement of related molecular biology tools, molecular marker-assisted breeding of coconut has become the next focus of coconut breeding, and accurate coconut phenotypic traits measurement will provide technical support for screening and identifying the correspondence between genotype and phenotype. A Micro-CT system was developed to measure coconut fruits and seeds automatically and nondestructively to acquire the 3D model and phenotyping traits. A deeplabv3+ model with an Xception backbone was used to segment the sectional image of coconut fruits and seeds automatically. Compared with the structural-light system measurement, the mean absolute percentage error of the fruit volume and surface area measurements by the Micro-CT system was 1.87% and 2.24%, respectively, and the squares of the correlation coefficients were 0.977 and 0.964, respectively. In addition, compared with the manual measurements, the mean absolute percentage error of the automatic copra weight and total biomass measurements was 8.85% and 25.19%, respectively, and the adjusted squares of the correlation coefficients were 0.922 and 0.721, respectively. The Micro-CT system can nondestructively obtain up to 21 agronomic traits and 57 digital traits precisely.
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