Background

Optical techniques, including computer vision, spectral imaging, near-infrared technology and other emerging imaging and spectroscopy techniques, have been rapidly developing and widely applied in fruit and vegetable grading systems for nondestructive quality inspecting and grading over the past decades. However, automatic detection of quality and grading is still difficult due to some still existing challenges, which are the key of blocking their commercialization in robotic fruit and vegetable grading systems. The challenges include the following aspects: the influence of physical and biological variability, whole surface detection, discrimination between defects and stems/calyxes, unobvious defect detection, robustness of the features and algorithms, as well as rapid optical detection system development. These challenges can reduce the fruit or vegetable quality inspection accuracy, thus greatly reducing automatic level of the quality inspecting and grading machines.

Scope and approach

As agricultural engineers with about eight years of technical experience in fruit grading systems, we believe the ultimate goal of each scientific research should seek its task in serving the engineering. So, we have made many attempts to solve the challenges and increase the automation of the grading machines.

Key findings and conclusions

The review gives a detailed summary about the challenges and solutions of optical-based nondestructive quality inspection for fruit or vegetable grading systems from the perspective of engineering. Particular attention has been paid to the techniques that can improve the automation degree of the grading robot in this review. The advantages and disadvantages of the solutions are compared and discussed. Additionally, the remaining engineering challenges and future trends are also discussed.

中文翻译：

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机器人果蔬分级系统基于光学的无损质量检查的挑战和解决方案：技术回顾

背景

在过去的几十年中，包括计算机视觉，光谱成像，近红外技术以及其他新兴的成像和光谱技术在内的光学技术已经得到迅速发展，并广泛应用于果蔬分级系统中，以进行无损质量检查和分级。但是，由于仍然存在一些挑战，因此自动检测质量和分级仍然很困难，这是阻止其在机器人水果和蔬菜分级系统中商业化的关键。挑战包括以下方面：物理和生物学变异性的影响，整个表面检测，缺陷与茎/花萼之间的区别，缺陷检测不明显，功能和算法的鲁棒性以及快速的光学检测系统开发。

范围和方法

作为在水果分级系统方面拥有大约八年技术经验的农业工程师，我们相信每项科学研究的最终目标都应寻求为工程服务的任务。因此，我们进行了许多尝试来解决挑战，并提高分级机的自动化程度。

主要发现和结论

这篇综述从工程学的角度对水果或蔬菜分级系统的基于光学的无损质量检查的挑战和解决方案进行了详细的总结。在这篇综述中，已经特别关注可以提高分级机器人的自动化程度的技术。比较并讨论了解决方案的优缺点。此外，还讨论了剩余的工程挑战和未来趋势。