Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize

Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 June 2025 V1 Latest version Share on Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize Authors : Rudan Xu 0000-0002-4980-1453 , John Ferguson , Johannes Kromdijk 0000-0003-4423-4100 , and Zoran Nikoloski [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175100405.52153616/v1 218 views 184 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Hyperspectral reflectance provides rapid and precise phenotyping of plants in a non-destructive manner both in field and well-controlled settings. The resulting data have been used to devise machine learning (ML) models for paired measurements of different traits in diverse plants and crops. Yet, despite advances in using of hyperspectral data to reliably predict crop traits of interest, there are pressing issues concerning the training of ML models, the aggregation of data from crop field trials, and the generalizability of the models in different prediction settings. We collected hyperspectral reflectance data along with 25 anatomical, gas exchange, and chlorophyll fluorescence traits from 320 recombinant inbred lines of a maize Multi-Parent Advanced Generation Inter-Cross population grown across three consecutive seasons. We use these data to systematically: (1) compare the performance of representative ML models for different traits, including slow fluorescence kinetics whose predictability by hyperspectral data has not yet been investigated, (2) evaluate the ML model performance in prediction scenarios concerning unseen genotypes, unseen seasons, and the combination thereof, (3) investigate the effects of data aggregation of ML model performance. These problems are addressed in a rigorous nested cross-validation setting that provides a template for adequate assessment of performance of ML models for diverse crop traits considering the particularities of the experimental design. Supplementary Material File (hsr_main_document.docx) Download 1.62 MB Information & Authors Information Version history V1 Version 1 27 June 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords zea mays chlorophyll fluorescence gas exchange hyperspectral reflectance photosynthesis: carbon reactions photosynthesis: electron transport Authors Affiliations Rudan Xu 0000-0002-4980-1453 Universitat Potsdam Institut fur Biochemie und Biologie View all articles by this author John Ferguson University of Essex School of Life Sciences View all articles by this author Johannes Kromdijk 0000-0003-4423-4100 University of Cambridge Department of Plant Sciences View all articles by this author Zoran Nikoloski [email protected] Universitat Potsdam Institut fur Biochemie und Biologie View all articles by this author Metrics & Citations Metrics Article Usage 218 views 184 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rudan Xu, John Ferguson, Johannes Kromdijk, et al. Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize. Authorea . 27 June 2025. 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