Convolutional neural network allows amylose content prediction in yam (Dioscorea alata L.) flour using near infrared spectroscopy - Houngbo, M.E.; Desfontaines, L.; Diman, J.L.; Arnau, G.; Mestres, C.; Davrieux, F.; Rouan, L.; Beurier, G.; Marie-Magdeleine, C.; Meghar, K.; Alamu, E.O.; Otegbayo, B.; et al. (Wiley, 2024)
Background: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone where it is grown. The lack of phenotyping methods for tuber quality hinders the adoption of new genotypes from the breeding programs. Recently, near infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product.