Accurate surface defect extraction of apples is critical for their quality inspection and marketing purposes. Using multi-band images, this study proposes a detection method for apple surface defects with a combination of machine vision and deep lear...
Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can...
Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on imag...
A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detec...
In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, pote...
The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface d...
At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-q...
Aiming at the problem of high false detection and missed detection rate of apple surface defects in complex environments, a new apple surface defect detection network: space-to-depth convolution-Multi-scale Empty Attention-Context Guided Feature Pyra...
This research proposes an apple quality grading approach based on multi-dimensional view information processing using YOLOv5s network as the framework to rapidly and accurately perform the apple quality grading task. The Retinex algorithm is employed...
Traditional machine vision is widely used to identify apple quality, but this method finds it difficult to distinguish the apple stem and calyx from defects. To address this, we designed a new method to identify the stem and calyx of apples based on...
Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world’s most consumed crops and with the current challenges of...
The persistence of Listeria monocytogenes biofilms on equipment surfaces poses a significant risk of cross-contamination, necessitating effective surface decontamination strategies. This study assessed the effectiveness of hurdle treatments combining...
Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for...
We have designed a high-resolution magnetic field imaging system using 256 unpackaged Hall elements. These unpackaged Hall elements are arranged in a Hall linear array, and the distance between adjacent elements reaches 255 µm. The sensitivity...