A Machine Vision Method for Detecting Pineapple Fruit Mechanical Damage

Jiahao Li, Baofeng Mai, Tianhu Liu (), Zicheng Liu, Zhaozheng Liang and Shuyang Liu
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Jiahao Li: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Baofeng Mai: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Tianhu Liu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zicheng Liu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Zhaozheng Liang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Shuyang Liu: College of Agriculture, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2025, vol. 15, issue 10, 1-16

Abstract: In the mechanical harvesting process, pineapple fruits are prone to damage. Traditional detection methods struggle to quantitatively assess pineapple damage and often operate at slow speeds. To address these challenges, this paper proposes a pineapple mechanical damage detection method based on machine vision, which segments the damaged region and calculates its area using multiple image processing algorithms. First, both color and depth images of the damaged pineapple are captured using a RealSense depth camera, and their pixel information is aligned. Subsequently, preprocessing techniques such as grayscale conversion, contrast enhancement, and Gaussian denoising are applied to the color images to generate grayscale images with prominent damage features. Next, an image segmentation method that combines thresholding, edge detection, and morphological processing is employed to process the images and output the damage contour images with smoother boundaries. After contour-filling and isolation of the smaller connected regions, a binary image of the damaged area is generated. Finally, a calibration object with a known surface area is used to derive both the depth values and pixel area. By integrating the depth information with the pixel area of the binary image, the damaged area of the pineapple is calculated. The damage detection system was implemented in MATLAB, and the experimental results showed that compared with the actual measured damaged area, the proposed method achieved an average error of 5.67% and an area calculation accuracy of 94.33%, even under the conditions of minimal skin color differences and low image resolution. Compared to traditional manual detection, this approach increases detection speed by over 30 times.

Keywords: machine vision; image processing; damage detection; pineapple (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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