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

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

Introduction

In contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.

Methods

For automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.

Results

These models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.

Discussion

This research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.

An improved Deeplab V3+ network based coconut CT image segmentation method

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth.

Research on the evolutionary history of the morphological structure of cotton seeds: a new perspective based on high-resolution micro-CT technology

Cotton (Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation (P< 0.001) between the cotton seed size and that of the seed kernel and seed coat volume, with correlation coefficients ranging from 0.51 to 0.92, while the cavity volume has a lower correlation with other phenotype indicators (r<0.37, P< 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.