Year: 2024
Type: article
Abstract: As the demand for apples increases, so do the expectations for apple quality. To address the time-consuming and labor-intensive nature of apple grading, this paper focuses on the Jin-Guan apple variety and applies machine vision techniques to extract three key features: maximum transverse diameter, shape index, and surface color. A backpropagation neural network (BP neural network) is employed to achieve accurate grading of the apples. First, by analyzing the relationships between the RGB color space components in the original images, the apple images are extracted using the method of comparing the R channel value to the B channel value. Gaussian filtering is then applied to remove image noise. Second, the complete apple contour is extracted using an improved Sobel operator method. The maximum transverse diameter and shape index of the apple are calculated based on the pixel coordinates along the contour, and the apple's surface color is determined using a nearest-neighbor classifier. Finally, a BP neural network model is established and trained to perform the grading. A total of 160 apples were used for training, and 140 for testing. The results show that the grading accuracy reached 92.14%, and the grading efficiency was approximately 1.7 times that of a skilled human grader. This grading method provides a technical reference for apple grading processes.
DOI: 10.54097/wn8s4k97
Author: Yi Shi
Institution: Shandong University of Technology
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4404474449