Early apple bruise recognition based on near-infrared imaging and grayscale gradient images

Abstract

Early apple bruises, especially those occurring within half an hour, usually have no external symptoms and are di cult to nd. In this study, a fast and nondestructive detection method for early bruises based on a near-infrared camera and image recognition was developed. A total of thirty apple samples were photographed on both sides of each apple. Grayscale images of the apples were captured using a nearinfrared camera with a wavelength region between 900 and 2350 nm. Images of apples (n = 62) without bruises were collected. The same apples were arti cially damaged and photographed by the near-infrared camera immediately. The apples were photographed again at 30-35 min after bruising, and a total of 186 grayscale images were collected. As the glossiness of apples limits the accuracy in the detection of defects, a compound method was proposed consisting of nonlinear grayscale transformation and frequency-domain image ltering techniques, followed by the rst derivative to obtain the gradient grayscale image. Since bruises had distinct edges, bruise edge pixels were detected instead of sound bruise pixels. The compound method obtained a 97.62% classi cation accuracy for nonbruised apples and apples with fresh bruises. The experimental results show that it is feasible to identify early bruises in apples based on near-infrared camera imaging and gradient grayscale images. The method can also provide a reference for the in-situ nondestructive early bruise detection of apples and other fruits.

Early Bruise Detection in Apple Based on an Improved Faster RCNN Model

“…The above research findings of different scholars on apple bruise detection using HSI of wavelengths in the range of 400-1000 nm with machine learning or deep learning algorithms, achieved promising research results. In addition, significant progress has been made in SWIR spectral bands, particularly beyond 1000 nm [28]. Keresztes et al [29] used a hyperspectral imaging system (1000-2500 nm) to monitor apples within 1 to 36 h after damage under five different impact forces.…”

Detection of early bruises on apples using near‐infrared camera imaging technology combined with adaptive threshold segmentation algorithm

“…In order to achieve accurate and fast apple grading, Xu et al (2023) improved the generalization ability, convergence speed and feature extraction ability of YOLOv5s model, with the grading accuracy of 93% and the grading speed of four apples per second. Yang, Yuan, et al (2023) proposed an improved YOLOv7 model which was inserted the CSPResNeXt-50 module and VoVGSCSP module, further improving the detection accuracy and speed for maize pests, with mAP reaching 76.3% and the detection speed reaching 67 frames per second (FPS). Li, Chen, et al (2023) achieved accurate detection and counting of Chinese cabbage trichomes based on the trinocular stereo microscope and the improved YOLOv8 model, with an accuracy of 94.4% which is 3.8% higher than YOLOv8n.…”