Raki Hind, Aalaila Yahya, Taktour Ayoub, Peluffo-Ordóñez Diego H
College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.
Materials Sciences and Nanotechnoloy (MSN), University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.
Foods. 2023 Dec 19;13(1):11. doi: 10.3390/foods13010011.
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically.
在全球范围内，考虑到食品供应链，食品安全和保障方面需要在从农场到餐桌的整个连续过程中加以考量。总体而言，农业食品系统是一个由相互关联的特征和过程组成的多重网络，预测难度较大，其中维持食品安全是不可或缺的要素，也是可持续发展目标（SDGs）的一部分。这促使科学界开发先进的应用分析方法，例如应用于评估食源性疾病的机器学习（ML）和深度学习（DL）技术。本文的主要目的是从分析角度为关于人工智能（AI）工具在粮食作物安全领域应用的正在进行的研究的共识版本的发展做出贡献。为一个更具体的主题撰写全面的综述也可能具有挑战性，尤其是在文献中进行搜索时。据我们所知，本综述是首次探讨这一问题。这项工作包括使用我们的方法对文献进行独特而详尽的研究。根据我们的纳入和排除标准，对所有与我们主题相关的现有文献进行了调查。对数据论文的最终计数进行了深入阅读和分析，以提取回答我们研究问题所需的信息。尽管已经进行了许多研究，但专门概述人工智能工具与基于作物的食品安全分析策略相结合的应用的关注却很有限。
