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Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

André Silva Aguiar, Sandro Augusto Magalhães, Filipe Neves dos Santos, Luis Castro, Tatiana Pinho, João Valente, Rui Martins, José Boaventura-Cunha
2021 Agronomy  
Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the  ...  The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%.  ...  In the annotation procedure, two classes were considered for grape bunches, given that two different growth stages were captured during the experiments: tiny-grape-bunch, representing grape bunches at  ... 
doi:10.3390/agronomy11091890 fatcat:gpbmajga7fhrdnnxq6entapwcq

Classification of Wine Grape Based on Different Phytosanitary Status by Using Visible/Near Infrared Spectroscopy

V. Giovenzana, R. Beghi, L. Brancadoro, R. Guidetti
2017 Chemical Engineering Transactions  
The quantification of diseases on wine grapes is commonly performed by a visual evaluation of the infection symptoms in grape bunches.  ...  The aim of this work was to investigate the applicability of vis/NIR spectroscopy for a rapid assessment of phytosanitary status of grape bunches directly at the check point at the grape consignment.  ...  and a model combining all the analysed bunches.  ... 
doi:10.3303/cet1758056 doaj:59556555108d40bcabdc90657b22906d fatcat:2jjfescznfhzhk54pxdp2rlg2q

Hyperspectral Imaging to Assess the Presence of Powdery Mildew (Erysiphe necator) in cv. Carignan Noir Grapevine Bunches

Pérez-Roncal, López-Maestresalas, Lopez-Molina, Jarén, Urrestarazu, Santesteban, Arazuri
2020 Agronomy  
and infected pixels distinction within grape bunches.  ...  Thirty Carignan Noir grape bunches, 15 healthy and 15 infected, were analyzed using a lab-scale HSI system (900–1700 nm spectral range).  ...  Coop for providing access to the bunch samples used in this study. Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/agronomy10010088 fatcat:hk4wdfqsevhlpogz37a7vymsd4

Investigating the Effects of Table Grape Package Components and Stacking on Airflow, Heat and Mass Transfer Using 3-D CFD Modelling

Mulugeta A. Delele, Mduduzi E. K. Ngcobo, Umezuruike Linus Opara, Chris J. Meyer
2012 Food and Bioprocess Technology  
The result demonstrated clearly the applicability of CFD models to determine optimum table grape packaging and cooling procedures.  ...  The carton box was explicitly modelled, grape bunch with the carry bag was treated as a porous medium and perforated plastic liners were taken as a porous jump.  ...  Improving the Grape Packaging and Cooling Procedure The validated model was applied to study alternative grape packaging and cooling procedures.  ... 
doi:10.1007/s11947-012-0895-5 fatcat:do6vockcj5dorcnwp44eaj4s4m

SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment

Jinhai Wang, Zongyin Zhang, Lufeng Luo, Wenbo Zhu, Jianwen Chen, Wei Wang
2021 Horticulturae  
very dense fruits, such as a grape bunch.  ...  This paper provides Swin Transformer and DETR models to achieve grape bunch detection. Additionally, they are compared with traditional CNN models, such as Faster-RCNN, SSD, and YOLO.  ...  Grape harvesting is a time-consuming and labor-intensive procedure [2, 3] .  ... 
doi:10.3390/horticulturae7110492 fatcat:54fqxmi3cjhe7ks27hzki66f4u

The Efficiency of the Moiré Technique for Three-Dimensional Measures of Grape Bunches

Juliana Aparecida Fracarolli, Kátia Cristina Suzigan, Inácio Maria Dal Fabbro
2016 Journal of Agricultural Science and Technology: B  
The artificial grapes with a 20 cm long bunch and 18.45 mm average diameter for each grape were used. A projector was used to generate a grid with light and dark lines.  ...  This result makes it possible to obtain size and shape of the grape bunch, allowing for the process automation of product selection and classification.  ...  The 3D model obtained through the Moiré technique can generate a mathematical description of the grape bunch.  ... 
doi:10.17265/2161-6264/2016.04.004 fatcat:zfookc3odvhf5ggh4z3xiscpfy

Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks

Andries J. Daniels, Carlos Poblete-Echeverría, Hélène H. Nieuwoudt, Nicolene Botha, Umezuruike Linus Opara
2021 Frontiers in Plant Science  
These results open up new possibilities for the development of quality checks of packed table grape bunches before export.  ...  friction browning based on the spectra obtained from intact 'Regal Seedless' table grape bunches that were cold-stored over different periods.  ...  This study was supported by the South African Table Grape  ... 
doi:10.3389/fpls.2021.768046 pmid:34782830 pmcid:PMC8589818 fatcat:knamtxrdaja7fgcngicnbirge4

RAPD fingerprintingXylella fastidiosaPierce's disease strains isolated from a vineyard in North Florida

R Albibi, J Chen, O Lamikanra, D Banks, R.L Jarret, B.J Smith
1998 FEMS Microbiology Letters  
This study shows that RAPD fingerprinting is a useful tool to supplement the conventional symptoms-colony morphology-slow growth identification procedure routinely used to identify the PD pathogen. z  ...  to S11 10^20 Bunch grape This study Blanc du Bois S1 to S9 21^29 Bunch grape This study Lake Emerald S1 to S7 30^36 Bunch grape This study Black Spanish S1 to S12 37^48 Bunch grape This study DN3-43 S1  ...  to S10 49^58 Bunch grape This study RN2-65 S1 to S8 59^66 Bunch grape This study CA11-17 S1 to S10 67^76 Bunch grape This study DC1-56 S1 to S10 77^86 Bunch grape This study Thomson Seedless S1to S11  ... 
doi:10.1111/j.1574-6968.1998.tb13168.x fatcat:4nsmjuu5rvaf5ozui2o623xvvu

3DBunch: A novel iOS-smartphone application to evaluate the number of grape berries per bunch using image analysis techniques

Scarlett Liu, Xiangdong Zeng, Mark Whitty
2020 IEEE Access  
Evaluating the number of berries per bunch is a vital step of grape yield estimation in viticulture but is a labour intensive task for traditional manual measurement.  ...  The application, called 3DBunch, acquires images from the camera or the album on a smartphone, and then estimates the number of berries by a reconstructed 3D bunch model based on the proposed image analysis  ...  The internal algorithm will be operated automatically by reconstructing the 3D grape bunch model.  ... 
doi:10.1109/access.2020.3003415 fatcat:kng5vqlcorhjxfbgr5sj7wt42u

End-to-End Automatic Berry Counting for Table Grape Thinning

Prawit Buayai, Kanda Runapongsa Saikaew, Xiaoyang Mao
2020 IEEE Access  
Bunch shape and berry size indicate the quality of table grapes and crucially affect their market value.  ...  Berry thinning is one of the most important tasks in grape cultivation to achieve an ideal bunch shape and to make sufficient space for individual berries.  ...  The post-processing procedure is depicted in Fig. 12 .  ... 
doi:10.1109/access.2020.3048374 fatcat:fgr5osplgnbntgepzcwwxkrlbe

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

Florian Rist, Doreen Gabriel, Jennifer Mack, Volker Steinhage, Reinhard Töpfer, Katja Herzog
2019 Remote Sensing  
For this reason, the present study is focused on the training and validation of different predictive regression models using 3D data from approximately 2000 different grape bunches in order to predict  ...  Grape bunch architecture is mainly influenced by the berry number, berry size, the total berry volume, and bunch width and length.  ...  Further reasons for model uncertainties might be differences in the experimental procedure between field and lab.  ... 
doi:10.3390/rs11242953 fatcat:pq63taicxbfuvar7ybbyms4bua

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

Johann Rose, Anna Kicherer, Markus Wieland, Lasse Klingbeil, Reinhard Töpfer, Heiner Kuhlmann
2016 Sensors  
A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy.  ...  In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.  ...  3 66 75.8 94.3 Grape Bunch 4 44 79.5 94.6 Grape Bunch 5 20 45.0 100 Grape Bunch 6 35 86.6 100 Grape Bunch 7 42 81.0 97.1 Grape Bunch 8 97 80.4 94.0 Grape Bunch 9 100 81.0 97.6  ... 
doi:10.3390/s16122136 pmid:27983669 pmcid:PMC5191116 fatcat:pahqdkbhfvbbtpzrwddzxur7pu

Using Bayesian growth models to predict grape yield

Rory Ellis, Elena Moltchanova, Daniel Gerhard, Mike Trought, LinLin Yang
2020 OENO One  
A Bayesian growth model, assuming a double sigmoidal curve, was used to predict the yield at the end of each season.  ...  Early yield prediction enables growers to manage vines to achieve target yields and prepare the required infrastructure for the harvest.Methods and results: Bunch mass data was collected during the 2016  ...  over time, due to the destructive measuring procedure.  ... 
doi:10.20870/oeno-one.2020.54.3.2972 fatcat:x437k6dh4zei3ijoujskx6cjxq

Optimization of NIR Spectral Data Management for Quality Control of Grape Bunches during On-Vine Ripening

Virginia González-Caballero, Dolores Pérez-Marín, María-Isabel López, María-Teresa Sánchez
2011 Sensors  
The feasibility of testing bunches of intact grapes was investigated and compared with the more traditional must-based method.  ...  A total of 363 samples from 25 white and red grape varieties were used to construct quality-prediction models based on reference data and on NIR spectral data obtained using a commercially-available diode-array  ...  Spectra were first obtained for intact bunches of grapes.  ... 
doi:10.3390/s110606109 pmid:22163944 pmcid:PMC3231454 fatcat:67le7mdwcfdxfjueaffz5fvef4

Machine Vision for Ripeness Estimation in Viticulture Automation

Eleni Vrochidou, Christos Bazinas, Michail Manios, George A. Papakostas, Theodore P. Pachidis, Vassilis G. Kaburlasos
2021 Horticulturae  
Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes.  ...  Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.  ...  Defoliation practices [59] could expose the grape bunches and facilitate grape bunch detection and removal.  ... 
doi:10.3390/horticulturae7090282 doaj:60a53d58958e451cadf3af519e6cf3c0 fatcat:uhgjpx6hwjg5pbpwrhyy5kozjm
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