New Insights into Recent Trends and Emerging Technologies in Apple Sorting

作者：Maninder Meenu, Pinki Choudhary, Priya Kaushal, Naveen Verma, Yogesh Kumar Yadav, Sahil Kalra, Hemani Sharma, Baojun Xu · 发表于：ACS Food Science & Technology · 年份：2024 · DOI：10.1021/acsfoodscitech.3c00552 · 被引用次数：5 · 研究领域：Spectroscopy and Chemometric Analyses、Smart Agriculture and AI、Leaf Properties and Growth Measurement

Apple sorting is a crucial step for their grading and determination of their commercial value. Sorting techniques are employed at several stages of production process, including harvest, storage, and packing. Manually apples are sorted and graded according to size and color, which is time-consuming, labor-intensive, and error-prone. Therefore, investigating nondestructive, precise, and effective methods of apple sorting is necessary to surmount the drawbacks connected with this. Technology advancements have led to the invention of several automated sorting techniques based on digital image processing, spectral and optical properties that are rapid and efficient compared to manual sorting. These technologies have advanced significantly for assessing the physical and nutritional quality of apples along with internal defects. In the present review, an overview of these advanced apple sorting methods is provided in detail. The contemporary techniques have improved apple sorting accuracy and efficiency while reducing labor costs and improving the quality. Computer vision systems can achieve high levels of accuracy and consistency by enabling nondestructive and quick assessment of many quality metrics, decreasing the subjectivity involved with human sorting. Spectral analysis and hyperspectral imaging have potential for determining interior characteristics like sugar content and ripeness. Robotics, machine learning, and sensor technologies can improve the efficiency, precision, and...