Review of deep learning-based methods for non-destructive evaluation of agricultural products

IF 4.4 1区农林科学 Q1 AGRICULTURAL ENGINEERING

Biosystems Engineering Pub Date : 2024-07-13 DOI:10.1016/j.biosystemseng.2024.07.002

Zhenye Li , Dongyi Wang , Tingting Zhu , Yang Tao , Chao Ni

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引用次数: 0

Abstract

Deep Learning (DL) has emerged as a pivotal modelling tool in various domains because of its proficiency in learning distributed representations. Numerous DL algorithms have recently been proposed and applied to non-destructive testing (NDT) methods in agriculture. This study aimed to review the state-of-the-art applications of DL algorithms in NDT by analysing the application of DL to specific NDT applications and highlighting their contributions and challenges. It first presents a comprehensive overview of various NDT techniques that have been combined with DL in agricultural product evaluation, and then briefly describes their applications in diverse NDT tasks, such as image classification, object detection, image retrieval, and semantic segmentation. Second, this study addresses the ongoing challenges associated with data collection and fusion, model complexity, computational requirements, and robustness. Finally, future research directions are examined, underscoring the potential of novel neural network architectures and cross-disciplinary collaborations. This review aims to provide a clear understanding of the current state of DL-based NDT in agricultural product examinations and its prospects for the future.

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基于深度学习的农产品无损评估方法综述

深度学习（DL）因其在学习分布式表征方面的能力，已成为各个领域中举足轻重的建模工具。最近，许多深度学习算法被提出并应用于农业无损检测（NDT）方法。本研究旨在通过分析 DL 在特定无损检测应用中的应用，回顾 DL 算法在无损检测中的最新应用，并强调其贡献和挑战。本研究首先全面概述了在农产品评估中与 DL 相结合的各种无损检测技术，然后简要介绍了这些技术在图像分类、物体检测、图像检索和语义分割等各种无损检测任务中的应用。其次，本研究探讨了与数据收集和融合、模型复杂性、计算要求和鲁棒性相关的持续挑战。最后，本研究探讨了未来的研究方向，强调了新型神经网络架构和跨学科合作的潜力。本综述旨在让人们清楚地了解农产品检测中基于 DL 的无损检测的现状及其未来前景。保留所有权利。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biosystems Engineering 农林科学-农业工程

CiteScore

10.60

自引率

7.80%

发文量

239

审稿时长

53 days

期刊介绍： Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.