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

Apples are highly susceptible to bruising during picking, packing, and transportation, and early detection of apple bruises allows timely screening and treatment to reduce food safety risks. However, early bruises on apples, especially bruises of 30 min or less, with similar appearance to sound tissue, making manual sorting difficult. To address this problem, a near-infrared (NIR) camera imaging technology combined with adaptive threshold segmentation algorithm for early apple bruise detection is proposed in this article. First, a NIR camera imaging system is built to acquire full-band images of fruit face, fruit stem face and calyx face of sound apples and apples with single bruise and multiple bruises at 30 min of occurrence. Then, an adaptive threshold segmentation algorithm is proposed and an image processing algorithm, which includes high-pass filtering, feature extraction, adaptive threshold segmentation and Hough circle detection, is developed and applied to full-band images in response to the interference of apple stem and calyx on early bruising detection in this article. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apple samples and an F1-score of 94.70% for the detection of early bruised samples, which is better than the commonly used image segmentation algorithms such as Otsu and fixed thresholding. This study shows that the combination of NIR camera imaging and the adaptive threshold segmentation algorithm proposed in this article can effectively detect early bruises on apples, providing a new idea for rapid and nondestructive detection of agricultural product quality.Practical applicationsDetecting early bruises on apples can achieve effective monitoring and control of apple supply chain, and guarantee the quality and safety of agricultural products. Most studies are based on conventional machine vision with RGB camera or hyperspectral imaging technology, while conventional machine vision based on RGB camera is difficult to detect apple early bruises that are not visible to the naked eye, and hyperspectral imaging has a large amount of data and redundant image data among wavelengths. Compared with previous studies, apple early bruise detection method proposed in this article combining NIR camera imaging technology and adaptive threshold segmentation algorithm can avoid the interference of bright spots, fruit stem and calyx on bruise segmentation while capturing a single channel image, providing a new method for accurate and rapid detection of early bruises on apples. A method was proposed to detect early bruises on apples using near-infrared (NIR) camera imaging combined with adaptive threshold segmentation algorithm. The results show that the proposed algorithm has an accuracy of 95.56% for the detection of sound apples and an F1-score of 94.70% for the detection of early bruised samples.image