Journal Article

Recent Advances and Applications of Nondestructive Testing in Agricultural Products: A Review

Han, Kang, Gu, Fei, Duan, Yanjun, Zhuang, Wenxu, Yin, Honglian, Jin, Xiaojun, Li, Mian

2025

Overview

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture.

• Publisher: MDPI AG
• Subject: Accuracy / Agricultural products / Agriculture / Algorithms / Artificial intelligence / Classification / Computed tomography / Computer vision / CT imaging / Deep learning / Electronic noses / Feature selection / Geospatial data / Hyperspectral imaging / Infrared imaging / Infrared spectra / Infrared spectroscopy / Inspection / Liu, Timothy / Machine learning / Machine vision / Medical imaging equipment / Near infrared radiation / Neural networks / Nondestructive testing / Product introduction / Product testing / Quality standards / Spectrum analysis / Support vector machines / Tomography / VOCs / Volatile organic compounds