Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis-Reference-Cited by-同舟云学术

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Identifying veraison process of colored wine grapes in field conditions combining deep learning and image analysis

Published:2022-09 Issue: Volume:200 Page:107268
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Shen Lei,Chen Shan,Mi Zhiwen,Su Jinya,Huang Rong,Song Yuyang,Fang Yulin,Su Baofeng

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference40 articles.

1. Bottou, L., 2012. Stochastic gradient descent tricks, Neural networks: Tricks of the trade. Springer, pp. 421-436.

2. End-to-end automatic berry counting for table grape thinning;Buayai;IEEE Access,2020

3. Segmentation of field grape bunches via an improved pyramid scene parsing network;Chen;Int. J. Agric. Biol. Eng.,2021

4. Grapevine phenology of cv. touriga franca and touriga nacional in the douro wine region: modelling and climate change projections;Costa;Agronomy,2019

5. An adaptable approach to automated visual detection of plant organs with applications in grapevine breeding;Grimm;Biosyst. Eng.,2019

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1. When crops meet machine vision: A review and development framework for a low-cost nondestructive online monitoring technology in agricultural production;Agriculture Communications;2024-03

2. An In-Field Dynamic Vision-Based Analysis for Vineyard Yield Estimation;IEEE Access;2024

3. Ripening dynamics revisited: an automated method to track the development of asynchronous berries on time-lapse images;Plant Methods;2023-12-14

4. Comparison of deep learning methods for grapevine growth stage recognition;Computers and Electronics in Agriculture;2023-08

5. A Grape Dataset for Instance Segmentation and Maturity Estimation;Agronomy;2023-07-27

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