Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

Abstract

Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods (– 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions.

Plant phenotyping relevance

ブドウ果実の外表構造変化を、スキャナ画像のテクスチャ解析と機械学習で評価する手法が研究の中心であり、植物器官の状態を画像から推定しているため。

Code and data availability

The paper's grape images, texture datasets, and models are not publicly deposited; the authors state data are available only upon request from the corresponding author. No public code or dataset URL is provided.

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