Multichannel imaging for monitoring chemical composition and germination capacity of cowpea(Vigna unguiculata) seeds during development and maturation

This study aimed to set a computer-integrated multichannel spectral imaging system as a high-throughput phenotyping tool for the analysis of individual cowpea seeds harvested at different developmental stages. The changes in germination capacity and variations in moisture, protein and different sugars during twelve stages of seed development from 10 to 32 days after anthesis were nondestructively monitored. Multispectral data at 20 discrete wavelengths in the ultraviolet, visible and near infrared regions were extracted from individual seeds and then modelled using partial least squares regression and linear discriminant analysis(LDA) models. The developed multivariate models were accurate enough for monitoring all possible changes occurred in moisture, protein and sugar contents with coefficients of determination in prediction R^(2) of 0.93, 0.80 and 0.78 and root mean square errors in prediction(RMSEP) of 6.045%, 2.236% and 0.890%, respectively. The accuracy of PLS models in predicting individual sugars such as verbascose and stachyose was reasonable with R~2 of 0.87 and 0.87 and RMSEP of 0.071%and 0.485%, respectively;but for the prediction of sucrose and raffinose the accuracy was relatively limited with R^(2) of 0.24 and 0.66 and RMSEP of 0.567% and 0.045%, respectively. The developed LDA model was robust in classifying the seeds based on their germination capacity with overall correct classification of96.33% and 95.67% in the training and validation datasets, respectively. With these levels of accuracy,the proposed multichannel spectral imaging system designed for single seeds could be an effective choice as a rapid screening and non-destructive technique for identifying the ideal harvesting time of cowpea seeds based on their chemical composition and germination capacity. Moreover, the development of chemical images of the major constituents along with classification images confirmed the usefulness of the proposed technique as a non-destructive tool for estimating the concentrations and spatial distributions of moisture, protein and sugars during different developmental stages of cowpea seeds.

GenoDrawing:An Autoencoder Framework for Image Prediction from SNP Markers

Advancements in genome sequencing have facilitated whole-genome characterization of numerous plant species,providing an abundance of genotypic data for genomic analysis.Genomic selection and neural networks(NNs),particularly deep learning,have been developed to predict complex traits from dense genotypic data.Autoencoders,an NN model to extract features from images in an unsupervised manner,has proven to be useful for plant phenotyping.This study introduces an autoencoder framework,GenoDrawing,for predicting and retrieving apple images from a low-depth single-nucleotide polymorphism(SNP)array,potentially useful in predicting traits that are difficult to define.GenoDrawing demonstrates proficiency in its task using a small dataset of shape-related SNPs.Results indicate that the use of SNPs associated with visual traits has substantial impact on the generated images,consistent with biological interpretation.While using substantial SNPs is crucial,incorporating additional,unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs.The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping,particularly beneficial for small to medium breeding companies to predict economically substantial heritable traits.Although GenoDrawing has limitations,it sets the groundwork for future research in image prediction from genomic markers.Future studies should focus on using stronger models for image reproduction,SNP information extraction,and dataset balance in terms of phenotypes for more precise outcomes.