End-to-End Inflorescence Measurement for Supporting Table Grape Trimming with Augmented Reality

Buayai, Prawit; Yok-In, Kabin; Inoue, Daisuke; Leow, Chee Siang; Nishizaki, Hiromitsu; Makino, Koji; Mao, Xiaoyang

doi:10.1109/cw52790.2021.00022

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…The rates of occlusion for both inflorescence and bunch exceed 50% by a significant margin [14]. DL models have made it possible to achieve non-destructive predictive models that can be used in uncontrolled environments, not only in terms of detecting and counting flowers per inflorescence, but also inflorescences per vine, since these are more robust, with better responses to occlusion and overlapping problems [15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Moreira,

Magalhães,

dos Santos

et al. 2023

Iecag 2023

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Section: Introductionmentioning

confidence: 99%

Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Moreira,

Magalhães,

dos Santos

et al. 2023

Iecag 2023

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“…To produce high-quality grapes, grapes need to be properly thinned to ensure that each grain of the grape has sufficient space to grow large. In addition, Buayai et al [14] developed an assistance system for organizing grape bunches before they become too large. This process is necessary to compact the bunches and ensure that sufficient nutrition is provided.…”

Section: Introductionmentioning

confidence: 99%

Appropriate grape color estimation based on metric learning for judging harvest timing

et al. 2022

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The color of a bunch of grapes is a very important factor when determining the appropriate time for harvesting. However, judging whether the color of the bunch is appropriate for harvesting requires experience and the result can vary by individuals. In this paper, we describe a system to support grape harvesting based on color estimation using deep learning. To estimate the color of a bunch of grapes, bunch detection, grain detection, removal of pest grains, and color estimation are required, for which deep learning-based approaches are adopted. In this study, YOLOv5, an object detection model that considers both accuracy and processing speed, is adopted for bunch detection and grain detection. For the detection of diseased grains, an autoencoder-based anomaly detection model is also employed. Since color is strongly affected by brightness, a color estimation model that is less affected by this factor is required. Accordingly, we propose multitask learning that uses metric learning. The color estimation model in this study is based on AlexNet. Metric learning was applied to train this model. Brightness is an important factor affecting the perception of color. In a practical experiment using actual grapes, we empirically selected the best three image channels from RGB and CIELAB (L*a*b*) color spaces and we found that the color estimation accuracy of the proposed multi-task model, the combination with “L” channel from L*a*b color space and “GB” from RGB color space for the grape image (represented as “LGB” color space), was 72.1%, compared to 21.1% for the model which used the normal RGB image. In addition, it was found that the proposed system was able to determine the suitability of grapes for harvesting with an accuracy of 81.6%, demonstrating the effectiveness of the proposed system.

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Grapevine inflorescence segmentation and flower estimation based on Computer Vision techniques for early yield assessment

Moreira,

Neves dos Santos,

Cunha

2025

Smart Agricultural Technology

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End-to-End Inflorescence Measurement for Supporting Table Grape Trimming with Augmented Reality

Cited by 9 publications

References 30 publications

Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Automated Infield Grapevine Inflorescence Segmentation Based on Deep Learning Models

Appropriate grape color estimation based on metric learning for judging harvest timing

Grapevine inflorescence segmentation and flower estimation based on Computer Vision techniques for early yield assessment

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