Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network

Abstract

Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.

Plant phenotyping relevance

リンゴ果実の輪郭・サイズを画像から抽出する手法を開発し、既存アルゴリズムと比較検証しており、植物形質取得が研究の中心である。

Code and data availability

The supplied blocks describe a 1500-image apple dataset and a rough-set + Faster-RCNN edge detection pipeline, but contain no data or code availability statement, no public repository, and no author-provided URL. No paper-specific public asset can be identified.

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