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 non-destructively 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 Rp2 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 Rp2 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 Rp2 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 of 96.33% 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.