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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Keywords: Viticulture, yield prediction, machine learning, deep learning.
Lucas Mohimont 1 ; Amine Chemchem 2 ; Marine Rondeau 3 ; Mathias Roesler 1 ; François Alin 1 ; Nathalie Gaveau 3 ; Luiz Angelo Steffenel 1

@article{ROIA_2021__2_1_33_0, author = {Lucas Mohimont and Amine Chemchem and Marine Rondeau and Mathias Roesler and Fran\c{c}ois Alin and Nathalie Gaveau and Luiz Angelo Steffenel}, title = {Traitement {d{\textquoteright}Images} et {Apprentissage} {Automatique} pour la {Viticulture} de {Pr\'ecision}}, journal = {Revue Ouverte d'Intelligence Artificielle}, pages = {33--63}, publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle}, volume = {2}, number = {1}, year = {2021}, doi = {10.5802/roia.9}, language = {fr}, url = {https://roia.centre-mersenne.org/articles/10.5802/roia.9/} }
TY - JOUR AU - Lucas Mohimont AU - Amine Chemchem AU - Marine Rondeau AU - Mathias Roesler AU - François Alin AU - Nathalie Gaveau AU - Luiz Angelo Steffenel TI - Traitement d’Images et Apprentissage Automatique pour la Viticulture de Précision JO - Revue Ouverte d'Intelligence Artificielle PY - 2021 SP - 33 EP - 63 VL - 2 IS - 1 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.9/ DO - 10.5802/roia.9 LA - fr ID - ROIA_2021__2_1_33_0 ER -
%0 Journal Article %A Lucas Mohimont %A Amine Chemchem %A Marine Rondeau %A Mathias Roesler %A François Alin %A Nathalie Gaveau %A Luiz Angelo Steffenel %T Traitement d’Images et Apprentissage Automatique pour la Viticulture de Précision %J Revue Ouverte d'Intelligence Artificielle %D 2021 %P 33-63 %V 2 %N 1 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.9/ %R 10.5802/roia.9 %G fr %F ROIA_2021__2_1_33_0
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, Introduction ROIA Agriculture Numérique, 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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