Smart Technologies for Pre- and Postharvest Quality Assessment of Fruits
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 25 September 2026 | Viewed by 184
Editors
Interests: green food and organic agricultural production; plant protection technology; fruit quality assessment; hyperspectral imaging; postharvest technology; artificial intelligence; machine learning; IoT; precision horticulture
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Interests: computer vision; image and signal processing; machine learning; embedded system; pest recognition and detection; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of artificial intelligence, robotics, and sensing technologies, smart systems are being increasingly applied to ensure fruit quality and safety throughout the entire production chain. This Special Issue, “Smart Technologies for Pre- and Postharvest Quality Assessment of Fruits,” aims to bring together cutting-edge research on the integration of intelligent perception, machine learning, and data-driven decision support for fruit quality monitoring, grading, and agronomically relevant quality management across the production system.
Topics of interest include, but are not limited to, the following: advanced imaging and spectral techniques for fruit inspection, Internet of Things (IoT) and edge computing applications in orchards and postharvest environments, in which fruit physiological responses under storage conditions are monitored;, deep learning models for defect detection and maturity assessment, and analysis of postharvest physiological processes during storage, focusing on how storage conditions influence fruit quality evolution in relation to preharvest agronomic practices. Studies addressing sustainability, traceability, and digital transformation in fruit production are also welcome.
This Special Issue aims to provide a comprehensive platform for researchers and practitioners to exchange novel ideas and technologies that enhance the efficiency, accuracy, and intelligence of fruit quality evaluation. Contributions involving interdisciplinary collaboration between agriculture, computer science, and engineering are particularly encouraged, provided that the research maintains a clear and explicit connection to agronomy, crop production systems, and field-to-postharvest physiological continuity.
Dr. Min Dong
Dr. Wenyong Li
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- smart agriculture
- fruit quality assessment
- hyperspectral imaging
- postharvest technology
- artificial intelligence
- machine learning
- IoT
- precision horticulture
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