{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T18:09:31Z","timestamp":1789063771507,"version":"build-2803163510"},"reference-count":29,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Animal Breeding Genetics"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Modern livestock breeding has mastered genotyping. Genome\u2010wide association studies, genomic selection, and SNP arrays enable genetic merit prediction at lower cost. However, phenotyping remains the bottleneck, as manual measurement is slow, expensive, subjective, and unable to capture spatial or temporal trait organization. Digital phenotyping via artificial intelligence could resolve this, but deep learning requires thousands of labelled examples, impractical when phenotyping cost itself limits datasets to hundreds of individuals. This creates a paradox: AI could accelerate phenotyping but requires large numbers of samples to train the models. Here, we demonstrate that integrating computer vision with machine learning offers sample\u2010efficient digital phenotyping using eggshell colour as a model system. Rather than learning features from scratch (deep learning), we engineer physically motivated features via Wavelet transforms that decompose images into multi\u2010scale spatial components. Wavelet features captured 14.2 percentage points more variance (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.976 vs. 0.834,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) than standard colorimetry, with 50% better sample efficiency (achieving at\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200960 what colorimetry required\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009120). Variance decomposition revealed 77% of discriminative capacity derives from spatial patterns (bands, spots, gradients) invisible to scalar averages. Additionally, we identified \u201ccryptic phenotypes\u201d (3.3%) where spatial patterns contradicted average colour, cases where colorimeters failed but Wavelets succeeded. The underlying principle\u2014that spatial decomposition can recover organizational information lost by scalar averaging\u2014may be applicable to other traits with spatial or temporal structure, such as marbling, dermatitis, or pigmentation rhythms, although whether comparable performance gains would be observed remains to be tested empirically. Hence, for breeding programs implementing genomic selection, computer vision\u2010based digital phenotyping captures complex trait variation without massive training datasets, addressing the bottleneck that increasingly limits genetic progress as genotyping becomes trivial.\n                  <\/jats:p>","DOI":"10.1111\/jbg.70076","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T13:35:42Z","timestamp":1789047342000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectral Transforms as a Tool to Optimize Digital Phenotyping in Biological Images"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-6799","authenticated-orcid":false,"given":"Joseane","family":"Padilha","sequence":"first","affiliation":[{"name":"Embrapa Swine and Poultry  Conc\u00f3rdia Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3168-3188","authenticated-orcid":false,"given":"Jose Victor Vieira","family":"Isola","sequence":"additional","affiliation":[{"name":"Embrapa Swine and Poultry  Conc\u00f3rdia Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elsio Antonio Pereira","family":"de Figueiredo","sequence":"additional","affiliation":[{"name":"Embrapa Swine and Poultry  Conc\u00f3rdia Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.07.006"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meatsci.2011.07.011"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.230"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.livsci.2019.103904"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21144749"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/jas\/sky418"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tplants.2011.09.005"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3168\/jds.2008-1646"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1751731109991091"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1357729800055089"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meatsci.2008.06.001"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/00071668.2021.2013439"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jsfa.2528"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1079\/WPS19870016"},{"key":"e_1_2_9_17_1","volume-title":"A Wavelet Tour of Signal Processing: The Sparse Way","author":"Mallat S.","year":"2009"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.192463"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.3389\/fvets.2022.965622"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.3382\/ps.2009\u201000261"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.2527\/af.2016-0002"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2016.03.011"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2020.09.002"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.3382\/ps\/pev202"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2008.05.016"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.applanim.2005.06.009"},{"key":"e_1_2_9_27_1","first-page":"2898","article-title":"Study on the Relationship Between Eggshell Colors and Egg Quality as Well as Shell Ultrastructure in Yangzhou Chicken","volume":"8","author":"Yang H. M.","year":"2009","journal-title":"African Journal of Biotechnology"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.molp.2020.01.008"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.3382\/ps\/pew051"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.03.014"}],"container-title":["Journal of Animal Breeding and Genetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jbg.70076","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/jbg.70076","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jbg.70076","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T17:50:39Z","timestamp":1789062639000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jbg.70076"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,10]]},"references-count":29,"alternative-id":["10.1111\/jbg.70076"],"URL":"https:\/\/doi.org\/10.1111\/jbg.70076","archive":["Portico"],"relation":{},"ISSN":["0931-2668","1439-0388"],"issn-type":[{"value":"0931-2668","type":"print"},{"value":"1439-0388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,10]]},"assertion":[{"value":"2026-02-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"jbg.70076"}}