Mohimont L., Alin F., Rondeau M., Gaveau N., Steffenel L.A. (2022). Computer vision and deep learning for precision viticulture. Agronomy, 01/10/2022, vol. 12, n. 10, p. 1-31.
https://doi.org/10.3390/agronomy12102463
https://doi.org/10.3390/agronomy12102463
Titre : | Computer vision and deep learning for precision viticulture (2022) |
Auteurs : | L. Mohimont ; F. Alin ; M. Rondeau ; N. Gaveau ; L.A. Steffenel |
Type de document : | Article |
Dans : | Agronomy (vol. 12, n. 10, October 2022) |
Article en page(s) : | p. 1-31 |
Langues : | Anglais |
Langues du résumé : | Anglais |
Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.5 - Gestion des ExploitationsThésaurus IAMM VITICULTURE ; AGRICULTURE DE PRECISION ; MODELE DE SIMULATION ; PREVISION DE RENDEMENT ; INTELLIGENCE ARTIFICIELLE |
Mots-clés: | APPRENTISSAGE AUTOMATIQUE |
Résumé : | During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task. |
Cote : | En ligne |
URL / DOI : | https://doi.org/10.3390/agronomy12102463 |