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.

Reçu le :
Révisé le :
Accepté le :
Publié le :
DOI : 10.5802/roia.9
Mot clés : Viticulture, prédictions de rendement, apprentissage automatique, apprentissage profond.
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

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
Licence : CC-BY 4.0
Droits d'auteur : Les auteurs conservent leurs droits
@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, Volume 2 (2021) no. 1, pp. 33-63. doi : 10.5802/roia.9. https://roia.centre-mersenne.org/articles/10.5802/roia.9/

[1] F. Abdelghafour; B. Keresztes; C. Germain; J. P. Da Costa Potential of on-board colour imaging for in-field detection and counting of grape bunches at early fruiting stages, Advances in Animal Biosciences, Volume 8 (2017) no. 22, pp. 505-509 | DOI

[2] F. Abdelghafour; R. Rosu; B. Keresztes; C. Germain; J. P. Da Costa A Bayesian framework for joint structure and colour based pixel-wise classification of grapevine proximal images, Computers and Electronics in Agriculture, Volume 158 (2019), pp. 345-357 | DOI

[3] Arturo Aquino; Ignacio Barrio; Maria-Paz Diago; Borja Millan; Javier Tardaguila vitisBerry : An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis, Computers and Electronics in Agriculture, Volume 148 (2018), pp. 19-28 | DOI

[4] Arturo Aquino; Maria P. Diago; Borja Millán; Javier Tardáguila A new methodology for estimating the grapevine-berry number per cluster using image analysis, Biosystems Engineering, Volume 156 (2017), pp. 80-95 | DOI

[5] Arturo Aquino; Borja Millan; Maria-Paz Diago; Javier Tardaguila Automated early yield prediction in vineyards from on-the-go image acquisition, Computers and Electronics in Agriculture, Volume 144 (2018), pp. 26-36 | DOI

[6] Arturo Aquino; Borja Millan; Daniel Gaston; María-Paz Diago; Javier Tardaguila vitisFlower : Development and Testing of a Novel Android-Smartphone Application for Assessing the Number of Grapevine Flowers per Inflorescence Using Artificial Vision Techniques, Sensors, Volume 15 (2015) no. 9, pp. 21204-21218 | DOI

[7] Arturo Aquino; Borja Millan; Salvador Gutiérrez; Javier Tardáguila Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis, Computers and Electronics in Agriculture, Volume 119 (2015), pp. 92-104 | DOI

[8] Sara Tokhi Arab; Ryozo Noguchi; Shusuke Matsushita; Tofael Ahamed Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach, Remote Sensing Applications : Society and Environment, Volume 22 (2021), 100485 https://www.sciencedirect.com/science/article/pii/S2352938521000215 | DOI

[9] Jaume Arnó; Martínez Casasnovas; Manel Ribes-Dasi; J. R. Rosell Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management, Spanish Journal of Agricultural Research, Volume 7 (2009), pp. 779-790 | DOI

[10] Dominique Arrouays; J. C. Begon; Bernard B. Nicoullaud; Christine Le Bas La variabilité des milieux, une réalité  : de la région à la plante, Perspectives Agricoles (1997) no. 222, pp. 8-12 | DOI | HAL

[11] Paynen A. B.; K. B. Walsh; P. P. Subedi; D. Jarvis Estimation of mango crop yield using image analysis – Segmentation method, Computers and Electronics in Agriculture, Volume 91 (2013), pp. 57-64 https://www.sciencedirect.com/science/article/pii/S0168169912002669 | DOI

[12] Suchet Bargoti; James Underwood Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards, Journal of Field Robotics, Volume 34 (2016), pp. 1039-1060 | DOI

[13] Mark Battany A Practical Method for Counting Berries based on Image Analysis, Proceedings of the 2nd Annual National Viticulture Research Conference (2008), pp. 4-5 | DOI

[14] Nasser Behroozi-Khazaei; Mohammad Reza Maleki A robust algorithm based on color features for grape cluster segmentation, Computers and Electronics in Agriculture, Volume 142 (2017), pp. 41-49 | DOI

[15] Enrico Bellocchio; Thomas A. Ciarfuglia; Gabriele Costante; Paolo Valigi Weakly Supervised Fruit Counting for Yield Estimation Using Spatial Consistency, IEEE Robotics and Automation Letters, Volume 4 (2019) no. 3, pp. 2348-2355 | DOI

[16] Ron Berenstein; Ohad Ben Shahar; Amir Shapiro; Yael Edan Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer, Intelligent Service Robotics, Volume 3 (2010) no. 44, pp. 233-243 | DOI

[17] Tom Botterill; Scott Paulin; Richard Green; Samuel Williams; Jessica Lin; Valerie Saxton; Steven Mills; XiaoQi Chen; Sam Corbett-Davies A Robot System for Pruning Grape Vines, Journal of Field Robotics, Volume 34 (2017) no. 6, pp. 1100-1122 https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21680 | DOI

[18] Mathilde Caron; Hugo Touvron; Ishan Misra; Hervé Jégou; Julien Mairal; Piotr Bojanowski; Armand Joulin Emerging Properties in Self-Supervised Vision Transformers (2021) (https://arxiv.org/abs/2104.14294)

[19] Vincent Casser Using Feedforward Neural Networks for Color Based Grape Detection in Field Images, CSCUBS 2016 - Computer Science Conference for University of Bonn Students (2016), pp. 23-33 /paper/Using-Feedforward-Neural-Networks-for-Color-Based-Casser/139ccba0b3a00565f61febcc62f98c6c44cca990 | DOI

[20] Hubert Cecotti; Agustin Rivera; Majid Farhadloo; Miguel A. Pedroza Grape detection with convolutional neural networks, Expert Systems with Applications, Volume 159 (2020), 113588 | DOI

[21] R. Chamelat; E. Rosso; A. Choksuriwong; C. Rosenberger; H. Laurent; P. Bro Grape Detection By Image Processing, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics (2006), pp. 3697-3702 | DOI

[22] S. W. Chen; S. S. Shivakumar; S. Dcunha; J. Das; E. Okon; C. Qu; C. J. Taylor; V. Kumar Counting Apples and Oranges With Deep Learning : A Data-Driven Approach, IEEE Robotics and Automation Letters, Volume 2 (2017) no. 2, pp. 781-788 | DOI

[23] Hong Cheng; Lutz Damerow; Yurui Sun; Michael Blanke Early Yield Prediction Using Image Analysis of Apple Fruit and Tree Canopy Features with Neural Networks, Journal of Imaging, Volume 3 (2017) no. 1, 6, 13 pages https://www.mdpi.com/2313-433X/3/1/6 | DOI

[24] P. R. Clingeleffer; S. R. Martin; G. M. Dunn; M. P. Krstic Crop development, crop estimation and crop control to secure quality and production of major wine grape varieties : a national approach, Adelaide, Grape and Wine Research and Development Corporation, 2001 | DOI

[25] Christian Correa; Constantino Valero; Pilar Barreiro Characterization of Vineyard’s Canopy through Fuzzy Clustering and SVM over Color Images, 3rd CIGR International Conference of Agricultural Engineering (CIGR-AgEng2012), Volume 1 (2012), 6 pages | DOI

[26] Luca Coviello; Marco Cristoforetti; Giuseppe Jurman; Cesare Furlanello GBCNet : In-Field Grape Berries Counting for Yield Estimation by Dilated CNNs, Applied Sciences, Volume 10 (2020) no. 1414, 4870, 15 pages | DOI

[27] D. Dey; L. Mummert; R. Sukthankar Classification of plant structures from uncalibrated image sequences, 2012 IEEE Workshop on the Applications of Computer Vision (WACV) (2012), pp. 329-336 | DOI

[28] Salvatore Filippo Di Gennaro; Piero Toscano; Paolo Cinat; Andrea Berton; Alessandro Matese A Low-Cost and Unsupervised Image Recognition Methodology for Yield Estimation in a Vineyard, Frontiers in Plant Science, Volume 10 (2019), 559 https://www.frontiersin.org/article/10.3389/fpls.2019.00559 | DOI

[29] M. P. Diago; A. Aquino; B. Millan; F. Palacios; J. Tardaguila On-the-go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis, Australian Journal of Grape and Wine Research, Volume 25 (2019) no. 3, pp. 363-374 | DOI

[30] Maria P Diago; Andres Sanz-Garcia; Borja Millan; Jose Blasco; Javier Tardaguila Assessment of flower number per inflorescence in grapevine by image analysis under field conditions, Journal of the Science of Food and Agriculture, Volume 94 (2014) no. 10, pp. 1981-1987 | DOI

[31] Maria P. Diago; Javier Tardaguila; Nuria Aleixos; Borja Millan; Jose M. Prats-Montalban; Sergio Cubero; Jose Blasco Assessment of cluster yield components by image analysis, Journal of the Science of Food and Agriculture, Volume 95 (2015) no. 66, pp. 1274-1282 | DOI

[32] Maria-Paz Diago; Christian Correa; Borja Millán; Pilar Barreiro; Constantino Valero; Javier Tardaguila Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions, Sensors, Volume 12 (2012) no. 12, pp. 16988-17006 | DOI

[33] P. Dolezel; P. Skrabanek; L. Gago Detection of grapes in natural environment using feedforward neural network as a classifier, 2016 SAI Computing Conference (SAI) (2016), pp. 1330-1334 | DOI

[34] Ulzii-Orshikh Dorj; Malrey Lee; Sang seok Yun An yield estimation in citrus orchards via fruit detection and counting using image processing, Computers and Electronics in Agriculture, Volume 140 (2017), pp. 103-112 https://www.sciencedirect.com/science/article/pii/S0168169916312455 | DOI

[35] Gregory M. Dunn; Stephen R. Martin Yield prediction from digital image analysis : A technique with potential for vineyard assessments prior to harvest, Australian Journal of Grape and Wine Research, Volume 10 (2004) no. 33, pp. 196-198 | DOI

[36] Davinia Font; Marcel Tresanchez; Dani Martínez; Javier Moreno; Eduard Clotet; Jordi Palacín Vineyard Yield Estimation Based on the Analysis of High Resolution Images Obtained with Artificial Illumination at Night, Sensors, Volume 15 (2015) no. 4, pp. 8284-8301 | DOI

[37] Patrick Kinyua Gikunda; Nicolas Jouandeau State-of-the-Art Convolutional Neural Networks for Smart Farms : A Review, Intelligent Computing (2019), pp. 763-775 | DOI

[38] Jonatan Grimm; Katja Herzog; Florian Rist; Anna Kicherer; Reinhard Töpfer; Volker Steinhage An adaptable approach to automated visual detection of plant organs with applications in grapevine breeding, Biosystems Engineering, Volume 183 (2019), pp. 170-183 | DOI

[39] Ben Grocholsky; Stephen Nuske; Matt Aasted; Supreeth Achar; Terry Bates A Camera and Laser System for Automatic Vine Balance Assessment, American Society of Agricultural and Biological Engineers Annual International Meeting 2011, ASABE 2011, Volume 7 (2011) | DOI

[40] Mathieu Grossetete; Yannick Berthoumieu; Jean-Pierre Da Costa; Christian Germain; Olivier Lavialle; Gilbert Grenier Early Estimation of Vineyard Yield : site specific counting of berries by using a smartphone, International Conference on Agiculture Engineering (AgEng) (2012), 143 | DOI | HAL

[41] Chris Hacking; Nitesh Poona; Nicola Manzan; Carlos Poblete-Echeverría Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation, Sensors, Volume 19 (2019) no. 17, 3652, 20 pages | DOI

[42] Kaiming He; Georgia Gkioxari; Piotr Dollár; Ross Girshick Mask R-CNN (2018) (https://arxiv.org/abs/1703.06870)

[43] Kai Heinrich; Andreas Roth; Lukas Breithaupt; Björn Möller; Johannes Maresch Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting, Wirtschaftsinformatik 2019 Proceedings (2019), pp. 407-421 https://aisel.aisnet.org/wi2019/track05/papers/3 | DOI

[44] Mónica Herrero-Huerta; Diego González-Aguilera; Pablo Rodriguez-Gonzalvez; David Hernández-López Vineyard yield estimation by automatic 3D bunch modelling in field conditions, Computers and Electronics in Agriculture, Volume 110 (2015), pp. 17-26 | DOI

[45] E. Ivorra; A. J. Sánchez; J. G. Camarasa; M. P. Diago; J. Tardaguila Assessment of grape cluster yield components based on 3D descriptors using stereo vision, Food Control, Volume 50 (2015), pp. 273-282 | DOI

[46] Andreas Kamilaris; Francesc X. Prenafeta-Boldú Deep learning in agriculture : A survey, Computers and Electronics in Agriculture, Volume 147 (2018), pp. 70-90 https://www.sciencedirect.com/science/article/pii/S0168169917308803 | DOI

[47] Barna Keresztes; Florent Abdelghafour; Dimby Randriamanga; Jean-Pierre Da Costa; Christian Germain Real-time Fruit Detection Using Deep Neural Networks, 14th International Conference on Precision Agriculture (2018) | DOI | HAL

[48] Barna Keresztes; Christian Germain; Jean-Pierre Da Costa; Gilbert Grenier; Xavier David-Beaulieu; Arnaud De La Fouchardière Vineyard Vigilant and INNovative Ecological Rover (VVINNER) : an autonomous robot for automated scoring of vineyards, International Conference of Agricultural Engineering (2014), 2098 | DOI | HAL

[49] Mohamed Kerkech; Adel Hafiane; Raphael Canals Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach, Computers and Electronics in Agriculture, Volume 174 (2020), p. 105446 https://www.sciencedirect.com/science/article/pii/S016816991932558X | DOI

[50] Anna Kicherer; Katja Herzog; Nele Bendel; Hans-Christian Klück; Andreas Backhaus; Markus Wieland; Johann Christian Rose; Lasse Klingbeil; Thomas Läbe; Christian Hohl; et al. Phenoliner : A New Field Phenotyping Platform for Grapevine Research, Sensors, Volume 17 (2017) no. 77, 1625, 18 pages | DOI

[51] Anna Kicherer; Katja Herzog; Nele Bendel; Hans-Christian Klück; Andreas Backhaus; Markus Wieland; Johann Christian Rose; Lasse Klingbeil; Thomas Läbe; Christian Hohl; Willi Petry; Heiner Kuhlmann; Udo Seiffert; Reinhard Töpfer Phenoliner : A New Field Phenotyping Platform for Grapevine Research, Sensors, Volume 17 (2017) no. 7, 1625, 18 pages https://www.mdpi.com/1424-8220/17/7/1625 | DOI

[52] Anna Kicherer; Katja Herzog; Michael Pflanz; Markus Wieland; Philipp Rüger; Steffen Kecke; Heiner Kuhlmann; Reinhard Töpfer An Automated Field Phenotyping Pipeline for Application in Grapevine Research, Sensors, Volume 15 (2015) no. 3, pp. 4823-4836 https://www.mdpi.com/1424-8220/15/3/4823 | DOI

[53] Anna Kicherer; Ribana Roscher; Katja Herzog; Silvio Å imon; Wolfgang Förstner; Reinhard Toepfer BAT (Berry Analysis Tool) : A high-throughput image interpretation tool to acquire the number, diameter, and volume of grapevine berries, Vitis -Geilweilerhof-, Volume 52 (2013), pp. 129-135 | DOI

[54] Maria Klodt; Katja Herzog; Reinhard Töpfer; Daniel Cremers Field phenotyping of grapevine growth using dense stereo reconstruction, BMC Bioinformatics, Volume 16 (2015) no. 1, 143 | DOI

[55] Alex Krizhevsky; Ilya Sutskever; Geoffrey E. Hinton ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS’12) (2012), pp. 1097-1105 | DOI

[56] P. Kurtser; O. Ringdahl; N. Rotstein; R. Berenstein; Y. Edan In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera, IEEE Robotics and Automation Letters, Volume 5 (2020) no. 2, 6, pp. 2031-2038 | DOI

[57] Yann LeCun; Bernhard E. Boser; John S. Denker; Donnie Henderson; R. E. Howard; Wayne E. Hubbard; Lawrence D. Jackel Handwritten Digit Recognition with a Back-Propagation Network (1990), pp. 396-404 http://papers.nips.cc/paper/293-handwritten-digit-recognition-with-a-back-propagation-network.pdf | DOI

[58] S. Liu; X. Zeng; M. Whitty 3DBunch : A Novel iOS-Smartphone Application to Evaluate the Number of Grape Berries per Bunch Using Image Analysis Techniques, IEEE Access, Volume 8 (2020), pp. 114663-114674 | DOI

[59] Scarlett Liu; Steve Cossell; Julie Tang; Gregory Dunn; Mark Whitty A computer vision system for early stage grape yield estimation based on shoot detection, Computers and Electronics in Agriculture, Volume 137 (2017), 1625, pp. 88-101 | DOI

[60] Scarlett Liu; Xuesong Li; Hongkun Wu; Bolai Xin; Julie Tang; Paul R. Petrie; Mark Whitty A robust automated flower estimation system for grape vines, Biosystems Engineering, Volume 172 (2018), 1625, pp. 110-123 https://www.sciencedirect.com/science/article/pii/S1537511017304610 | DOI

[61] Scarlett Liu; Samuel Marden; Mark Whitty Towards Automated Yield Estimation in Viticulture, Proceedings of the Australasian Conference on Robotics and Automation (2013), 9 pages | DOI

[62] Scarlett Liu; Mark Whitty Automatic grape bunch detection in vineyards with an SVM classifier, Journal of Applied Logic, Volume 13 (2015) no. 4, Part 34, Part 3, pp. 643-653 | DOI

[63] Scarlett Liu; Mark Whitty; Steve Cossell A Lightweight Method for Grape Berry Counting based on Automated 3 D Bunch Reconstruction from a Single Image, ICRA, International Conference on Robotics and Automation (IEEE), Workshop on Robotics in Agriculture (2015) | DOI

[64] Scarlett Liu; Xiangdong Zeng; Mark Whitty A vision-based robust grape berry counting algorithm for fast calibration-free bunch weight estimation in the field, Computers and Electronics in Agriculture, Volume 173 (2020), 105360, 11 pages | DOI

[65] X. Liu; S. W. Chen; C. Liu; S. S. Shivakumar; J. Das; C. J. Taylor; J. Underwood; V. Kumar Monocular Camera Based Fruit Counting and Mapping With Semantic Data Association, IEEE Robotics and Automation Letters, Volume 4 (2019) no. 3, 465, pp. 2296-2303 | DOI

[66] Jaime Lloret; Ignacio Bosch; Sandra Sendra; Arturo Serrano A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing, Sensors, Volume 11 (2011) no. 6, 905, pp. 6165-6196 https://www.mdpi.com/1424-8220/11/6/6165 | DOI

[67] Carlos Lopes; Albert Torres; Roberto Guzman; João Graça; Ana Monteiro; Ricardo Braga; Andre Barriguinha; Gonçalo Victorino; Miguel Reys Using an Unmanned Ground Vehicle to Scout Vineyards for Non-intrusive Estimation of Canopy Features and Grape Yield, 20th GiESCO International Meeting (2017)

[68] A. Lopez-Castro; A. Marroquin-Jacobo; A. Soto-Amador; E. Padilla-Davila; J. A. Lopez-Leyva; M. O. Castañeda-Ramos Design of a Vineyard Terrestrial Robot for Multiple Applications as Part of the Innovation of Process and Product : Preliminary Results, 2020 IEEE International Conference on Engineering Veracruz (ICEV) (2020), pp. 1-4 | DOI

[69] Lufeng Luo; Yunchao Tang; Qinghua Lu; Xiong Chen; Po Zhang; Xiangjun Zou A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard, Computers in Industry, Volume 99 (2018), pp. 130-139 https://www.sciencedirect.com/science/article/pii/S0166361517305298 | DOI

[70] Lufeng Luo; Yunchao Tang; Xiangjun Zou; Chenglin Wang; Po Zhang; Wenxian Feng Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components, Sensors, Volume 16 (2016) no. 12, 2098, 20 pages https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191078/ | DOI

[71] Lufeng Luo; Yunchao Tang; Xiangjun Zou; Min Ye; Wenxian Feng; Guoqing Li Vision-based extraction of spatial information in grape clusters for harvesting robots, Biosystems Engineering, Volume 151 (2016), pp. 90-104 https://www.sciencedirect.com/science/article/pii/S1537511015303901 | DOI

[72] Jennifer Mack; Christian Lenz; Johannes Teutrine; Volker Steinhage High-precision 3D detection and reconstruction of grapes from laser range data for efficient phenotyping based on supervised learning, Computers and Electronics in Agriculture, Volume 135 (2017), pp. 300-311 https://www.sciencedirect.com/science/article/pii/S0168169916308602 | DOI

[73] Walter Maldonado; José Carlos Barbosa Automatic green fruit counting in orange trees using digital images, Computers and Electronics in Agriculture, Volume 127 (2016), pp. 572-581 https://www.sciencedirect.com/science/article/pii/S0168169916305294 | DOI

[74] R. Marani; A. Milella; A. Petitti; G. Reina Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera, Precision Agriculture (2021), pp. 387-413 | DOI

[75] Alessandro Matese; Salvatore Filippo Di Gennaro Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture, Agriculture, Volume 8 (2018) no. 7, 116, 13 pages https://www.mdpi.com/2077-0472/8/7/116 | DOI

[76] Alessandro Matese; Salvatore Filippo Di Gennaro Technology in precision viticulture : a state of the art review, International Journal of Wine Research, Volume 7 (2015), pp. 69-81 https://www.dovepress.com/technology-in-precision-viticulture-a-state-of-the-art-review-peer-reviewed-article-IJWR | DOI

[77] Annalisa Milella; Roberto Marani; Antonio Petitti; Giulio Reina In-field high throughput grapevine phenotyping with a consumer-grade depth camera, Computers and Electronics in Agriculture, Volume 156 (2019), pp. 293-306 | DOI

[78] Borja Millan; Arturo Aquino; Maria P. Diago; Javier Tardaguila Image analysis-based modelling for flower number estimation in grapevine, Journal of the Science of Food and Agriculture, Volume 97 (2017) no. 3, 116, pp. 784-792 | DOI

[79] Borja Millan; Santiago Velasco-Forero; Arturo Aquino; Javier Tardaguila On-the-Go Grapevine Yield Estimation Using Image Analysis and Boolean Model, Journal of Sensors, Volume 2018 (2018), 9634752, 15 pages | DOI

[80] Eduardo A. Murillo-Bracamontes; Miguel E. Martinez-Rosas; Manuel M. Miranda-Velasco; Horacio L. Martinez-Reyes; Jesus R. Martinez-Sandoval; Humberto Cervantes-de-Avila Implementation of Hough transform for fruit image segmentation, Procedia Engineering, Volume 35 (2012), pp. 230-239 | DOI

[81] A. K. Nellithimaru; G. A. Kantor ROLS : Robust Object-Level SLAM for Grape Counting, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019), pp. 2648-2656 | DOI

[82] S. Nuske; S. Achar; T. Bates; S. Narasimhan; S. Singh Yield estimation in vineyards by visual grape detection, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (2011), 969, pp. 2352-2358 | DOI

[83] Stephen Nuske; Kyle Wilshusen; Supreeth Achar; Luke Yoder; Sanjiv Singh Automated Visual Yield Estimation in Vineyards, J. Field Robot., Volume 31 (2014) no. 55, pp. 837-860 | DOI

[84] Fernando Palacios; Maria P. Diago; Javier Tardaguila A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions, Sensors, Volume 19 (2019) no. 17, 3799 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6749308/ | DOI

[85] Josman P. Pérez-Expósito; Tiago M. Fernández-Caramés; Paula Fraga-Lamas; Luis Castedo VineSens : An Eco-Smart Decision-Support Viticulture System, Sensors, Volume 17 (2017) no. 3, 465, 26 pages | DOI

[86] Rodrigo Pérez-Zavala; Miguel Torres-Torriti; Fernando Auat Cheein; Giancarlo Troni A pattern recognition strategy for visual grape bunch detection in vineyards, Computers and Electronics in Agriculture, Volume 151 (2018), pp. 136-149 | DOI

[87] S. K. Pilli; B. Nallathambi; S. J. George; V. Diwanji eAGROBOT - A robot for early crop disease detection using image processing, 2014 International Conference on Electronics and Communication Systems (ICECS) (2014), 559, pp. 1-6 | DOI

[88] G. Rabatel; C. Guizard Grape berry calibration by computer vision using elliptical model fitting, ECPA 2007, 6th European Conference on Precision Agriculture (2007), pp. 581-587 | DOI | HAL

[89] A. Rahman; A. Hellicar Identification of mature grape bunches using image processing and computational intelligence methods, 2014 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP) (2014), pp. 1-6 | DOI

[90] Maryam Rahnemoonfar; Clay Sheppard Deep Count : Fruit Counting Based on Deep Simulated Learning, Sensors, Volume 17 (2017) no. 4, 905, 12 pages https://www.mdpi.com/1424-8220/17/4/905 | DOI

[91] M. J. C. S. Reis; R. Morais; E. Peres; C. Pereira; O. Contente; S. Soares; A. Valente; J. Baptista; P. J. S. G. Ferreira; J. Bulas Cruz Automatic detection of bunches of grapes in natural environment from color images, Journal of Applied Logic, Volume 10 (2012) no. 44, pp. 285-290 | DOI

[92] Olaf Ronneberger; Philipp Fischer; Thomas Brox U-Net : Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (Lecture Notes in Computer Science) (2015), pp. 234-241 | DOI

[93] Ribana Roscher; Katja Herzog; Annemarie Kunkel; Anna Kicherer; Reinhard Töpfer; Wolfgang Förstner Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields, Computers and Electronics in Agriculture, Volume 100 (2014), pp. 148-158 | DOI

[94] Johann Christian Rose; Anna Kicherer; Markus Wieland; Lasse Klingbeil; Reinhard Töpfer; Heiner Kuhlmann Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions, Sensors, Volume 16 (2016) no. 12, 2136, 25 pages | DOI

[95] Robert Rudolph; Katja Herzog; Reinhard Töpfer; Volker Steinhage Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks, Journal of Grapevine Research, Volume 58 (2019) no. 3, 3799, pp. 95-104 | DOI

[96] Thiago Santos; Luis Bassoi; Henrique Oldoni; Roberto Martins Automatic grape bunch detection in vineyards based on affordable 3D phenotyping using a consumer webcam, XI Congresso Brasileiro de Agroinformática (SBIAgro 2017), 2017 | DOI

[97] Thiago T. Santos; Leonardo L. de Souza; Andreza A. dos Santos; Sandra Avila Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association, Computers and Electronics in Agriculture, Volume 170 (2020), 105247 | DOI

[98] K. P. Seng; L. Ang; L. M. Schmidtke; S. Y. Rogiers Computer Vision and Machine Learning for Viticulture Technology, IEEE Access, Volume 6 (2018), pp. 67494-67510 | DOI

[99] E. Shelhamer; J. Long; T. Darrell Fully Convolutional Networks for Semantic Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 39 (2017) no. 4, pp. 640-651 | DOI

[100] Daniel L. Silver; Tanya Monga In Vino Veritas : Estimating Vineyard Grape Yield from Images Using Deep Learning, Advances in Artificial Intelligence (Lecture Notes in Computer Science) (2019), 4870, pp. 212-224 | DOI

[101] Pavel Škrabánek DeepGrapes : Precise Detection of Grapes in Low-resolution Images, IFAC-PapersOnLine, Volume 51 (2018) no. 66, 113588, pp. 185-189 | DOI

[102] Pavel Škrabánek; Petr Doležel Robust Grape Detector Based on SVMs and HOG Features, Computational Intelligence and Neuroscience, Volume 2017 (2017), 3478602 | DOI

[103] Y. Song; C. A. Glasbey; G. W. Horgan; G. Polder; J.A. Dieleman; G. W. A. M. van der Heijden Automatic fruit recognition and counting from multiple images, Biosystems Engineering, Volume 118 (2014), pp. 203-215 https://www.sciencedirect.com/science/article/pii/S1537511013002109 | DOI

[104] Javier Tello; Katja Herzog; Florian Rist; Patrice This; Agnès Doligez Automatic Flower Number Evaluation in Grapevine Inflorescences Using RGB Images, American Journal of Enology and Viticulture, Volume 71 (2019), pp. 10-16 https://www.ajevonline.org/content/early/2019/09/12/ajev.2019.19036 | DOI

[105] J. P. Vasconez; J. Delpiano; S. Vougioukas; F. Auat Cheein Comparison of convolutional neural networks in fruit detection and counting : A comprehensive evaluation, Computers and Electronics in Agriculture, Volume 173 (2020), 105348 https://www.sciencedirect.com/science/article/pii/S016816991932232X | DOI

[106] Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N Gomez; Łukasz Kaiser; Illia Polosukhin Attention is All you Need, Advances in Neural Information Processing Systems, Volume 30 (2017), 105360 https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf | DOI

[107] Gonçalo Victorino; Guilherme Maia; José Queiroz; Ricardo Braga; Jorge Marques; Carlos Lopes Grapevine yield prediction using image analysis – improving the estimation of non-visible bunches, European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) (2019), 105247, p. 6 | DOI

[108] Qi Wang; Stephen Nuske; Marcel Bergerman; Sanjiv Singh Automated Crop Yield Estimation for Apple Orchards (2013), 105348, pp. 745-758 | DOI

[109] Juntao Xiong; Zhen Liu; Rui Lin; Rongbin Bu; Zhiliang He; Zhengang Yang; Cuixiao Liang Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination, Sensors, Volume 18 (2018) no. 4, 969, 17 pages https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948586/ | DOI

[110] Laura Zabawa; Anna Kicherer; Lasse Klingbeil; Reinhard Töpfer; Heiner Kuhlmann; Ribana Roscher Counting of grapevine berries in images via semantic segmentation using convolutional neural networks, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 164 (2020), 100485, pp. 73-83 | DOI

[111] Kai Zhang; Li Zhao; Sun Zhe; Chang Geng; Wei Li Design and Experiment of Intelligent Grape Bagging Robot, Applied Mechanics and Materials, Volume 389 (2013), pp. 706-711 | DOI

[112] P. Zwaenepoel; J.M. Le Bars L’agriculture de précision, Ingénieries eau-agriculture-territoires (1997) no. 12, pp. 67-79 | DOI | HAL

Cité par Sources :