{"id":2240,"date":"2026-01-05T11:46:15","date_gmt":"2026-01-05T16:46:15","guid":{"rendered":"https:\/\/wp.well-e.org\/publication\/predicting-dairy-profitability-using-deep-learning-models\/"},"modified":"2026-10-23T08:47:22","modified_gmt":"2026-10-23T12:47:22","slug":"predicting-dairy-profitability-using-deep-learning-models","status":"publish","type":"publication","link":"https:\/\/wp.well-e.org\/fr\/publication\/predicting-dairy-profitability-using-deep-learning-models\/","title":{"rendered":"Predicting dairy profitability using deep learning models"},"content":{"rendered":"<p><strong>Authors:<\/strong> Vahid Naghashi<\/p>\n<p><strong>Date:<\/strong> 2025-01-01<\/p>\n<p><strong>Status:<\/strong> Published<\/p>\n<p><a href=\"http:\/\/archipel.uqam.ca\/19882\/1\/D5068.pdf\" target=\"_blank\" rel=\"noopener\">View External Publication<\/a><\/p>\n<hr>\n<p>Dans la gestion de pr\u00e9cision de l\u2019\u00e9levage, une prise de d\u00e9cision efficace concernant le remplacement des animauxd\u00e9pendd\u2019uneestimationpr\u00e9cisedelarentabilit\u00e9 \u00e0viedechaqueanimal.Enproductionlaiti\u00e8re, la quantit\u00e9 de lait produite est influenc\u00e9e par divers facteurs, notamment la qualit\u00e9 du lait, l\u2019\u00e9tat de sant\u00e9, la g\u00e9n\u00e9tique et les pratiques de gestion du troupeau, lesquels peuvent \u00eatre affect\u00e9s par des dynamiques op\u00e9rationnelles et de march\u00e9 plus larges. Cette th\u00e8se formule l\u2019estimation des revenus de production laiti\u00e8re comme un probl\u00e8me de pr\u00e9vision de s\u00e9ries temporelles multivari\u00e9es, o\u00f9 la rentabilit\u00e9 future \u2014 g\u00e9n\u00e9ralement mesur\u00e9e lors des lactations ult\u00e9rieures (par exemple, lactation 2 ou 3) \u2014 est pr\u00e9dite \u00e0 partir des donn\u00e9es recueillies pendant les premi\u00e8res p\u00e9riodes de lactation (par exemple, lactation 1). Les donn\u00e9es disponibles comprennent un ensemble de variables laiti\u00e8res ordonn\u00e9es temporellement, collect\u00e9es sur plusieurs mois apr\u00e8s la naissance de la vache. Ce jeu de donn\u00e9es spatio-temporel peut \u00eatre trait\u00e9 comme une s\u00e9rie temporelle multivari\u00e9e, o\u00f9 chaque variable (ou canal) repr\u00e9sente un facteur laitier distinct \u00e9voluant dans le temps. La r\u00e9solution de cette t\u00e2che de pr\u00e9vision n\u00e9cessite des mod\u00e8les capables de capturer simultan\u00e9ment les d\u00e9pendances temporelles et les corr\u00e9lations inter-caract\u00e9ristiques (entre canaux). Cette th\u00e8se explore des architectures d\u2019apprentissage profond pour la pr\u00e9vision de s\u00e9ries temporelles, en mettant particuli\u00e8rement l\u2019accent sur leur application \u00e0 la pr\u00e9diction des revenus laitiers. Apr\u00e8s avoir pass\u00e9 en revue les approches existantes, notamment les mod\u00e8les autor\u00e9gressifs, les r\u00e9seaux de neurones r\u00e9currents (RNN), les mod\u00e8les lin\u00e9aires et les mod\u00e8les bas\u00e9s sur les Transformers, nous introduisons plusieurs architectures nouvelles con\u00e7uespour la pr\u00e9vision multivari\u00e9e. Premi\u00e8rement, nous proposons une architecture d\u00e9riv\u00e9e des LSTM, enrichie d\u2019une couche d\u2019attention pour mod\u00e9liser les motifs s\u00e9quentiels et contextuels dans le cadre de la pr\u00e9diction laiti\u00e8re. Deuxi\u00e8mement, nous introduisons deux mod\u00e8les bas\u00e9s sur les Transformers : l\u2019un ax\u00e9 sur la mod\u00e9lisation temporelle multi-\u00e9chelle et inter-canaux, et l\u2019autre mettant l\u2019accent sur les interactions entre canaux tout en les int\u00e9grant aux motifs temporels captur\u00e9s. Enfin, nous pr\u00e9sentons un mod\u00e8le r\u00e9current qui applique des GRU de mani\u00e8re bidirectionnelle le long de la dimension des canaux, afin de capter les d\u00e9pendances complexes entre caract\u00e9ristiques, en particulier dans les sc\u00e9narios de pr\u00e9vision laiti\u00e8re. Tous les mod\u00e8les propos\u00e9s sont \u00e9valu\u00e9s de mani\u00e8re approfondie sur des jeux de donn\u00e9es de r\u00e9f\u00e9rence publics ainsi que sur des donn\u00e9es r\u00e9elles provenantd\u2019exploitations laiti\u00e8res. Les r\u00e9sultats montrent que nos m\u00e9thodes surpassent ou \u00e9galent syst\u00e9matiquement les mod\u00e8les de pointe actuels en termes de pr\u00e9cision de pr\u00e9vision et d\u2019efficacit\u00e9 computationnelle. Au-del\u00e0 des contributions m\u00e9thodologiques, ce travail soutient une meilleure allocation des ressources et une prise de d\u00e9cision plus \u00e9clair\u00e9e dans l\u2019industrie laiti\u00e8re, en offrant une base fond\u00e9e sur les donn\u00e9es pour une estimation plus pr\u00e9cise de la rentabilit\u00e9 et une gestion du troupeau plus rentable.<\/p>\n","protected":false},"template":"","meta":{"_acf_changed":false},"class_list":["post-2240","publication","type-publication","status-publish","hentry"],"blocksy_meta":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predicting dairy profitability using deep learning models - WELL-E<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/wp.well-e.org\/fr\/publication\/predicting-dairy-profitability-using-deep-learning-models\/\" \/>\n<meta property=\"og:locale\" content=\"fr_CA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicting dairy profitability using deep learning models - WELL-E\" \/>\n<meta property=\"og:description\" content=\"Authors: Vahid Naghashi Date: 2025-01-01 Status: Published View External Publication Dans la gestion de pr\u00e9cision de l\u2019\u00e9levage, une prise de d\u00e9cision efficace concernant le remplacement des animauxd\u00e9pendd\u2019uneestimationpr\u00e9cisedelarentabilit\u00e9 \u00e0viedechaqueanimal.Enproductionlaiti\u00e8re, la quantit\u00e9 de lait produite est influenc\u00e9e par divers facteurs, notamment la qualit\u00e9 du lait, l\u2019\u00e9tat de sant\u00e9, la g\u00e9n\u00e9tique et les pratiques de gestion du troupeau, [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/wp.well-e.org\/fr\/publication\/predicting-dairy-profitability-using-deep-learning-models\/\" \/>\n<meta property=\"og:site_name\" content=\"WELL-E\" \/>\n<meta property=\"article:modified_time\" content=\"2026-10-23T12:47:22+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/predicting-dairy-profitability-using-deep-learning-models\\\/\",\"url\":\"https:\\\/\\\/wp.well-e.org\\\/publication\\\/predicting-dairy-profitability-using-deep-learning-models\\\/\",\"name\":\"Predicting dairy profitability using deep learning models - 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