A non-destructive coconut fruit and seed traits extraction method based on Micro-CT and deeplabV3+ model.

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.

Plant phenotyping relevance

Micro-CTとDeepLabV3+によるココナッツ果実・種子の非破壊的な3D形質取得システムを開発し、他の測定法および手動測定と比較検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper describes a Micro-CT coconut phenotyping system with a DeepLabV3+ segmentation model, but no public dataset, image, or code deposit with an authors' URL is provided. The data availability statement only offers raw data from the authors upon request, so the paper-specific raw data qualifies only as request_on.

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