Traitement d’Images et Apprentissage Automatique pour la Viticulture de Précision
Revue Ouverte d'Intelligence Artificielle, Volume 2 (2021) no. 1, pp. 33-63.

Au cours des dernières décennies, les chercheurs ont mis au point des méthodes informatiques novatrices pour aider les viticulteurs à résoudre leurs problèmes, principalement liés à la prévision des rendements avant la récolte. L’objectif de cet article est de résumer les travaux de recherche existants liés aux techniques de vision par ordinateur qui traitent de ces questions. Il se concentre sur les approches utilisant des images RGB obtenues directement sur les parcelles, en utilisant différentes méthodes allant des algorithmes classiques de traitement d’image, en passant par les approches d’apprentissage automatique jusqu’à de nouvelles méthodes basées sur l’apprentissage profond. Nous avons l’intention de fournir un examen complet accessible également aux lecteurs non spécialisés, afin de discuter des progrès récents de l’intelligence artificielle dans la viticulture. À cette fin, nous présenterons dans les premières parties les travaux portant sur la détection des fleurs ainsi que des grappes et des raisins, puis dans la dernière partie nous présenterons les différentes méthodes d’estimation du rendement et les problématiques associées.

An early yield prediction is an essential tool for vinegrowers to prepare for the harvest. In the last decades, many methods have been proposed by researchers to solve this problem practically. In particular, recent computer vision advances allow for fruit counting directly in the field. The objective of this publication is to make an exhaustive review of existing works related to grape detection and yield prediction with computer vision algorithms. This review is focused on approaches using RGB proximal images, including classical image processing algorithms, feature extraction, machine learning models, and deep learning. By recalling works on flower counting, grape detection, berry counting, and yield modelization, we highlight challenges on computer vision research for viticulture applications. In addition, we believe that non-specialists can find this review helpful to understand recent advances in artificial intelligence for precision viticulture.

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DOI : 10.5802/roia.9
Mots clés : Viticulture, prédictions de rendement, apprentissage automatique, apprentissage profond.
Lucas Mohimont 1 ; Amine Chemchem 2 ; Marine Rondeau 3 ; Mathias Roesler 1 ; François Alin 1 ; Nathalie Gaveau 3 ; Luiz Angelo Steffenel 1

1 Université de Reims Champagne Ardenne, Laboratoire LICIIS - LRC CEA DIGIT, 51097 Reims Cedex 2, France
2 Pôle Intelligence Artificielle, ATOS. Rue du Mas de Verchant, 34000 Montpellier
3 Université de Reims Champagne Ardenne, Laboratoire RIBP - USC INRAE 1488 51687 Reims Cedex 2, France
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Lucas Mohimont; Amine Chemchem; Marine Rondeau; Mathias Roesler; François Alin; Nathalie Gaveau; Luiz Angelo Steffenel. Traitement d’Images et Apprentissage Automatique pour la Viticulture de Précision. Revue Ouverte d'Intelligence Artificielle, Volume 2 (2021) no. 1, pp. 33-63. doi : 10.5802/roia.9. https://roia.centre-mersenne.org/articles/10.5802/roia.9/

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