Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning

Journal article

IntroductionAccurate measurement of dietary intake without interfering in natural eating habits is a long-standing problem in nutritional epidemiology. We explored the applicability of hyperspectral imaging and machine learning for dietary assessment of home-prepared meals, by building a proof-of-concept, which automatically detects food ingredients inside closed sandwiches.MethodsIndividual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.ResultsThe resulting best performing models had an accuracy score of ~80% for predicting type of bread, ~60% for butter, and ~ 28% for filling type. We see that the main struggle in predicting the fillings lies with the spreadable fillings, meaning the model may be focusing on structural aspects and not nutritional composition.DiscussionFurther analysis on non-homogeneous mixed food items, using computer vision techniques, will contribute toward a generalizable system. While there are still significant technical challenges to overcome before such a system can be routinely implemented in studies of free-living subjects, we believe it holds promise as a future tool for nutrition research and population intake monitoring

Authors: Furbank, RT; Silva-Perez, V; Evans, JR; Condon, AG; Estavillo, GM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Plant Methods
• Volume: 17
• Issue: 1
• Pages: 108-108
• Article number: 108
• Publication date: 2021-10-19
• DOI: 10.1186/s13007-021-00806-6
• EISSN: 1746-4811
• ISSN: 1746-4811
• Language: English
• Keywords: Biology; Reflectivity; Optics; Remote sensing; Hyperspectral imaging; Geology; Environmental science