{"id":234712,"date":"2025-04-29T18:08:33","date_gmt":"2025-04-29T16:08:33","guid":{"rendered":"https:\/\/blog.lewagon.com\/?p=234712"},"modified":"2025-04-29T18:08:33","modified_gmt":"2025-04-29T16:08:33","slug":"lagtech-la-data-au-service-de-lagriculture","status":"publish","type":"post","link":"https:\/\/blog.lewagon.com\/fr\/data\/lagtech-la-data-au-service-de-lagriculture\/","title":{"rendered":"L’AgTech: La data au service de l’agriculture"},"content":{"rendered":"","protected":false},"excerpt":{"rendered":"

Lors d’un \u00e9v\u00e9nement au campus de Montr\u00e9al, nous avons r\u00e9uni des experts de l\u2019agtech afin d\u2019\u00e9changer sur l\u2019usage des donn\u00e9es dans le domaine de l\u2019agriculture. Autour de la table \u00e9taient pr\u00e9sents: Colline Blanc, Data Scientist, Justin Hogue et John Lan, tous deux R&D Scientific chez ChrysaLabs. La discussion \u00e9tait anim\u00e9e par Hugo Dupuis, Directeur des programmes \u00e0 la Zone 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d'un \u00e9v\u00e9nement au campus de Montr\u00e9al, nous avons r\u00e9uni des experts de l\u2019agtech afin d\u2019\u00e9changer sur l\u2019usage des donn\u00e9es dans le domaine de l\u2019agriculture. Autour de la table \u00e9taient pr\u00e9sents: Colline Blanc, Data Scientist, Justin Hogue et John Lan, tous deux R&D Scientific chez ChrysaLabs. La discussion \u00e9tait anim\u00e9e par Hugo Dupuis, Directeur des programmes \u00e0 la Zone Agtech.\u00a0<\/span>\r\n\r\nL\u2019agriculture de demain commence par une meilleure compr\u00e9hension des sols. Gr\u00e2ce \u00e0 l\u2019IA, il est d\u00e9sormais possible de<\/span> collecter et interpr\u00e9ter une masse d'informations autrefois difficilement accessibles<\/b>.<\/span>\r\n

\u00ab On voulait utiliser l\u2019intelligence artificielle pour pr\u00e9dire la fertilit\u00e9 des sols \u00e0 partir d\u2019une signature spectrale, sans avoir besoin d\u2019analyser chaque \u00e9chantillon en laboratoire \u00bb,<\/span><\/i> explique Colline, qui travaille au d\u00e9veloppement des mod\u00e8les d\u2019IA chez ChrysaLabs.<\/span><\/blockquote>\r\nPour cela, l\u2019\u00e9quipe a utilis\u00e9 des r\u00e9seaux de neurones et des algorithmes d\u2019apprentissage automatique afin de <\/span>trouver des liens entre les mesures spectrales du sol et ses propri\u00e9t\u00e9s r\u00e9elles<\/b>, comme le niveau d\u2019azote ou de potassium.<\/span>\r\n\r\n \r\n

De la science du sol \u00e0 l\u2019IA : quand la rigueur pr\u00e9c\u00e8de l\u2019innovation<\/b><\/h2>\r\nChez ChrysaLabs, l\u2019intelligence artificielle n\u2019est pas une fin en soi. Elle est un outil mis au service d\u2019une compr\u00e9hension fine des sols.\u00a0<\/span>\r\n
\u00ab L\u2019objectif n\u2019est pas d\u2019entra\u00eener n\u2019importe quel mod\u00e8le avec n\u2019importe quelle donn\u00e9e. Il faut d\u2019abord<\/span><\/i> comprendre ce qu\u2019on cherche \u00e0 pr\u00e9dire<\/i><\/b> \u00bb, insiste John, R&D Scientific.<\/span><\/i><\/blockquote>\r\nJustin, \u00e9galement R&D Scientific, rench\u00e9rit :\u00a0<\/span>\r\n
\u00ab <\/span><\/i>Le mod\u00e8le n\u2019est qu\u2019un outil<\/i><\/b>. Le c\u0153ur, c\u2019est la science des sols. Il faut un bon jeu de donn\u00e9es, repr\u00e9sentatif, bien \u00e9tiquet\u00e9. Ensuite, l\u2019IA peut faire son travail. \u00bb<\/span><\/i><\/blockquote>\r\nMais mod\u00e9liser des ph\u00e9nom\u00e8nes complexes en agriculture, ce n\u2019est pas une mince affaire. Colline, Data Scientist, souligne la difficult\u00e9 de la t\u00e2che :\u00a0<\/span>\r\n
\u00ab La variabilit\u00e9 est \u00e9norme. Un sol n\u2019est jamais identique d\u2019un champ \u00e0 l\u2019autre, m\u00eame parfois \u00e0 quelques m\u00e8tres de distance. \u00bb<\/span><\/i><\/blockquote>\r\nPour relever ces d\u00e9fis, l\u2019\u00e9quipe a adopt\u00e9 une approche hybride, m\u00ealant <\/span>rigueur scientifique et cr\u00e9ativit\u00e9 technologique<\/b>.\u00a0<\/span>\r\n
\u00ab On a explor\u00e9 plein de pistes, du simple mod\u00e8le lin\u00e9aire aux r\u00e9seaux de neurones profonds. <\/span><\/i>Parfois, le mod\u00e8le le plus simple est aussi le plus efficace<\/i><\/b> \u00bb, confie Justin.\u00a0<\/span><\/i><\/blockquote>\r\nNettoyage des donn\u00e9es, validation crois\u00e9e, mod\u00e8les d\u2019ensemble\u2026 chaque \u00e9tape est pens\u00e9e pour garantir robustesse et pr\u00e9cision.<\/span>\r\n\r\n \r\n

Des mod\u00e8les intelligents, ancr\u00e9s dans la r\u00e9alit\u00e9 agricole<\/b><\/h2>\r\nL\u2019\u00e9quipe de ChrysaLabs travaille main dans la main avec les agronomes, les producteurs et les partenaires de terrain. L\u2019objectif : <\/span>cr\u00e9er des outils utiles, ancr\u00e9s dans la r\u00e9alit\u00e9 agricole<\/b>.\u00a0<\/span>\r\n
\u00ab On ne veut pas que ce soit une bo\u00eete noire.<\/span><\/i> Il faut qu\u2019un agronome puisse interpr\u00e9ter les r\u00e9sultats <\/i><\/b>et les remettre dans le contexte du champ \u00bb, explique Colline.<\/span><\/i><\/blockquote>\r\nC\u2019est aussi pour cela que la collaboration avec les utilisateurs est constante. <\/span><\/i>\r\n
Justin<\/span><\/i> insiste : \u00ab<\/span><\/i> Il faut que notre travail fasse du sens <\/i><\/b>pour ceux qui sont sur le terrain. \u00c7a nous pousse \u00e0<\/span><\/i> rester connect\u00e9s \u00e0 la r\u00e9alit\u00e9 agricole.<\/i><\/b> \u00bb<\/span><\/i><\/blockquote>\r\nAvec ChrysaLabs, l\u2019IA devient un levier pour mieux comprendre, anticiper et accompagner la prise de d\u00e9cision \u2014 <\/span>mais toujours en dialogue avec l\u2019exp\u00e9rience humaine.<\/b>\r\n\r\n \r\n

Une aventure technologique et humaine<\/b><\/h2>\r\nChez ChrysaLabs, la recherche et le d\u00e9veloppement se vivent comme une exploration. Et parfois, cela veut dire\u2026 se tromper.\u00a0<\/span>\r\n
\u00ab On a fait beaucoup d\u2019essais-erreurs.<\/strong> On s\u2019est parfois rendu compte que certaines approches n\u2019\u00e9taient pas du tout adapt\u00e9es au contexte agricole \u00bb, reconna\u00eet John.<\/span><\/i><\/blockquote>\r\nMais chaque d\u00e9tour est aussi un apprentissage.\u00a0<\/b>\r\n
\u00ab C\u2019est un processus tr\u00e8s it\u00e9ratif. On teste, on ajuste, on recommence. <\/span><\/i>Et c\u2019est ce qui rend le travail si passionnant<\/i><\/b> \u00bb, ajoute Justin.<\/span><\/i><\/blockquote>\r\nL\u2019agilit\u00e9 est au c\u0153ur de leur m\u00e9thode<\/strong>, avec une forte capacit\u00e9 d\u2019adaptation \u00e0 la complexit\u00e9 du terrain, aux saisons, aux types de cultures. <\/span>\r\n
\u00ab <\/span>Il faut accepter l\u2019incertitude et travailler avec<\/span><\/i> \u00bb, r\u00e9sume <\/span>Colline.<\/span><\/blockquote>\r\n \r\n

Une collaboration \u00e9troite entre profils scientifiques<\/b><\/h2>\r\nUne autre richesse du projet vient de<\/span> la collaboration entre disciplines<\/b>. Les profils en sciences des sols, en science des donn\u00e9es et en ing\u00e9nierie travaillent main dans la main.<\/span>\r\n
\u00ab On avait chacun notre angle.<\/strong> Moi en tant que data scientist, j\u2019avais besoin de comprendre les capteurs, le type de donn\u00e9es, la physique derri\u00e8re. Les chercheurs en agronomie m\u2019ont aid\u00e9e \u00e0 relier ces points \u00bb, explique Colline.<\/span><\/i><\/blockquote>\r\nEt cette interdisciplinarit\u00e9 se ressent jusque dans les r\u00e9sultats : un outil de diagnostic des sols plus rapide, plus pr\u00e9cis, et qui permet aux agriculteurs <\/span>d\u2019ajuster leurs pratiques en temps r\u00e9el<\/b>.<\/span>\r\n\r\n \r\n\r\n[video width=\"1920\" height=\"1080\" mp4=\"https:\/\/blog.lewagon.com\/wp-content\/uploads\/2025\/04\/extrait-pour-le-blog-chrysalabs.mp4\"][\/video]\r\n\r\n \r\n\r\nDans cette vid\u00e9o, nos invit\u00e9s expliquent leur int\u00e9r\u00eat \u00e0 travailler en agriculture.\u00a0<\/span>\r\n\r\n \r\n

Et demain ?<\/b><\/h2>\r\n
\u00ab L\u2019IA n\u2019est pas une solution magique, mais c\u2019est<\/span><\/i> un levier puissant <\/i><\/b>pour transformer l\u2019agriculture \u00bb, conclut John.\u00a0<\/span><\/i><\/blockquote>\r\nL\u2019ambition de ChrysaLabs et de la Zone Agtech est claire :<\/span> rendre l\u2019agriculture plus efficiente, durable, et connect\u00e9e \u00e0 la r\u00e9alit\u00e9 terrain<\/b>.<\/span>\r\n\r\nEt surtout, faire en sorte que cette technologie soit au service de ceux qui nourrissent le monde.<\/strong><\/span>\r\n\r\n \r\n\r\n\ud83c\udfa7 <\/span>Envie d\u2019aller plus loin ? <\/b>\ud83d\udc49 D\u00e9couvrez le replay de la discussion ici<\/a> <\/span>\r\n\r\n \r\n\r\nVous souhaitez apprendre \u00e0 utiliser les donn\u00e9es et en faire votre m\u00e9tier? Explorez nos bootcamps <\/span>Data Analytics<\/span><\/a> et <\/span>Data Science & AI.\u00a0<\/span><\/a>","_contenu_de_larticle":"field_6412588b55814","titre_de_la_banniere":"","_titre_de_la_banniere":"field_6422eb732c237","paragraphe_de_la_banniere":"","_paragraphe_de_la_banniere":"field_6422ed834a968","intitule_du_bouton_de_la_banniere":"","_intitule_du_bouton_de_la_banniere":"field_6422ed934a969","lien_du_bouton_de_la_banniere":"","_lien_du_bouton_de_la_banniere":"field_6422eda54a96a","yoast_head":"\nL'AgTech: La data au service de l'agriculture | Blog Le Wagon<\/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:\/\/blog.lewagon.com\/fr\/data\/lagtech-la-data-au-service-de-lagriculture\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"L'AgTech: La data au service de l'agriculture\" \/>\n<meta property=\"og:description\" content=\"Lors d'un \u00e9v\u00e9nement au campus de Montr\u00e9al, nous avons r\u00e9uni des experts de l\u2019agtech afin d\u2019\u00e9changer sur l\u2019usage des donn\u00e9es dans le domaine de l\u2019agriculture. 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