The fruit and vegetable industry is undergoing significant challenges, particularly in maintaining quality assurance and market competitiveness. Non-destructive testing technologies offer sustainable solutions, making them a focus of research in agricultural engineering and food science. This research topic seeks to consolidate innovative research and practical insights on the intelligent evolution of non-destructive technologies for fruits and vegetables. With the improvement of consumers' requirements for fruit and vegetable quality, the traditional manual subjective evaluation detection method has been difficult to meet the needs of industry and market development. Fruit and vegetable non-destructive testing methods such as optical, acoustic, and electrical sensing allow for rapid, non-invasive quality assessment. For example, near-infrared spectroscopy technology can be used to detect attributes like hardness, soluble solids, and acidity, while machine vision technology can detect the appearance defects. Advancing the intelligence of these technology is crucial for adapting to the diverse characteristics of fruits and vegetables and production environment.

This research topic addresses key challenges in the intelligent process of fruit and vegetable non-destructive testing technology. We aim to delve into the physical principles and algorithm optimization behind non-destructive testing technology, especially by analyzing the key parameters and models of different testing techniques for obtaining internal quality information of fruits and vegetables. This covers the in-depth study of feature extraction algorithms for near-infrared spectroscopy, electrical property detection, and machine vision image recognition models. In addition, we are committed to understanding how to integrate these non-destructive testing approaches with intelligent algorithms, such as deep learning and machine learning, to heighten detection accuracy and efficiency. We also seek innovations in non-destructive testing equipment, leveraging advances in sensor technology and micro-electro-mechanical systems, coupled with AI-driven analytics, to enhance operational performance and portability. Finally, we emphasize real-world applications, focusing on how to embed these technologies within the fruit and vegetable supply chain, from harvest to logistics, ensuring comprehensive quality monitoring. By integrating the research results of many fields, we expect to build a complete set of intelligent evolution system of fruit and vegetable non-destructive testing.

This research topic focuses on the intelligence of fruit and vegetable non-destructive testing technology and its practical application. In particular, we look forward to submissions covering the following topics:

1) Application of new non-destructive testing technology and intelligent algorithm in fruit and vegetable quality testing;

2) The influence of different fruit and vegetable variety characteristics on the optimization of non-destructive testing technology and the parameter adjustment strategy;

3) Develop and evaluate innovative nondestructive testing equipment and systems, as well as their practical application effect in the fruit and vegetable industry chain.

4) Integration of cutting-edge artificial intelligence techniques, such as deep neural networks, into non-destructive testing technologies for enhancing data analysis, predictive modeling, and decision-making processes in fruit and vegetable quality assessment.

We welcome original research papers, comprehensive reviews as well as short newsletters that share new findings, experimental data or theoretical models related to these topics. The manuscript should have in-depth analysis to contribute to the development of knowledge in the field of fruit and vegetable nondestructive testing technology.

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Intelligent Evolution: Practice and Breakthrough of Non-destructive Testing Technology for Fruits and Vegetables