Huang Yuping, Li Ziang, Bian Zhouchen, Jin Haojun, Zheng Guoqing, Hu Dong, Sun Ye, Fan Chenlong, Xie Weijun, Fang Huimin
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
School of Flexible Electronics (Future Technologies) and Institute of Advanced Materials (IAM), Nanjing Tech University, Nanjing 211816, China.
Foods. 2025 Jan 16;14(2):286. doi: 10.3390/foods14020286.
Tomato, as the vegetable queen, is cultivated worldwide due to its rich nutrient content and unique flavor. Nondestructive technology provides efficient and noninvasive solutions for the quality assessment of tomatoes. However, processing the substantial datasets to achieve a robust model and enhance detection performance for nondestructive technology is a great challenge until deep learning is developed. The aim of this paper is to provide a systematical overview of the principles and application for three categories of nondestructive detection techniques based on mechanical characterization, electromagnetic characterization, as well as electrochemical sensors. Tomato quality assessment is analyzed, and the characteristics of different nondestructive techniques are compared. Various data analysis methods based on deep learning are explored and the applications in tomato assessment using nondestructive techniques with deep learning are also summarized. Limitations and future expectations for the quality assessment of the tomato industry by nondestructive techniques along with deep learning are discussed. The ongoing advancements in optical equipment and deep learning methods lead to a promising outlook for the application in the tomato industry and agricultural engineering.
番茄作为“蔬菜皇后”，因其丰富的营养成分和独特的风味而在全球范围内广泛种植。无损检测技术为番茄品质评估提供了高效、非侵入性的解决方案。然而，在深度学习发展之前，处理大量数据集以建立一个强大的模型并提高无损检测技术的检测性能是一项巨大的挑战。本文旨在系统概述基于机械表征、电磁表征以及电化学传感器的三类无损检测技术的原理及应用。分析了番茄品质评估，并比较了不同无损检测技术的特点。探索了基于深度学习的各种数据分析方法，并总结了深度学习在番茄无损检测技术评估中的应用。讨论了无损检测技术结合深度学习在番茄产业品质评估中的局限性和未来展望。光学设备和深度学习方法的不断进步为其在番茄产业和农业工程中的应用带来了广阔前景。
