Non-destructive prediction of protein contents of soybean seeds using near-infrared hyperspectral imaging

Protein content is one of the most crucial factors in soybean quality. However, the breeding procedure necessitates the time-consuming and costly selection of elite genotypes from many experimental lines in a destructive manner. The present work aims to predict protein content in single soybean seeds non-destructively using Near-Infrared (NIR) Hyperspectral Imaging (HSI). 1491 seed samples from 3 varieties of the low, medium, and high protein content (consisting of 371, 560, and 560 samples, respectively) were measured using the NIR-HSI system with a range of 900–1800 nm. The spectral data extracted from the HSI 3D hypercube were synchronised to the reference values examined from chemical analysis. The calibration model was constructed using partial least square regression (PLSR) methods based on the 70% spectral data and then validated using the remaining 30% of data. The result showed that the NIR-HSI technique is a promising method to predict protein content in soybean seeds, as shown by an R2 of 0.92 and a root mean square error (RMSE) of 1.08% . In addition, the chemical images visualised the distribution of protein content for the multiple soybean seed showed the possibility of the developed technique for the use of rapid evaluation of massive samples in the processing line

Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud

Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of Big Data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3-D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3-D point cloud. This study utilized four 3-D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (R2) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3-D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques