Paper
28 July 2023 Construction of a grape quality index from RGB images of crates
Soizic Lefevre, Danielle Nuzillard, Alban Goupil
Author Affiliations +
Proceedings Volume 12749, Sixteenth International Conference on Quality Control by Artificial Vision; 1274904 (2023) https://doi.org/10.1117/12.2688348
Event: Sixteenth International Conference on Quality Control by Artificial Vision, 2023, Albi, France
Abstract
Ranging the crates of grapes using a robust quality index is a major tool for operators during the Champagne grape harvest. We propose building such an index by processing RGB images of crates of grapes. Each image is segmented into six classes such as healthy grape, crate, diseases (grey rot, powdery mildew, conidia), green elements (stalk, leaf, unripe healthy grape), shadow, dry elements (dry leaf, dry grape, wood) and the index of quality reflects the proportion of healthy part inside the crate. As the main pretreatment, the segmentation must be carefully performed, and a random forest-based solution for each variety of grape is proposed here whose training is done on hand-tagged pixels.
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Soizic Lefevre, Danielle Nuzillard, and Alban Goupil "Construction of a grape quality index from RGB images of crates", Proc. SPIE 12749, Sixteenth International Conference on Quality Control by Artificial Vision, 1274904 (28 July 2023); https://doi.org/10.1117/12.2688348
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KEYWORDS
Random forests

Image classification

Databases

Image segmentation

Image processing

Image quality

RGB color model

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