Applications of Imaging Technology in Agriculture
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 329
Editors
Interests: artificial intelligence; green agri-food innovation; non-destructive analysis; plant-based materials; sustainable food systems
Interests: agricultural biotechnology; applied microbiology; fermentation engineering; food microbiology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the integration of advanced imaging technologies into modern agricultural systems to improve productivity, sustainability, and quality assessment. The primary focus is on non-destructive, real-time imaging approaches—such as hyperspectral imaging, multispectral imaging, thermal imaging, and computer vision—combined with artificial intelligence (AI) and data-driven analytics. The scope of this Special Issue covers a wide range of applications across the agricultural value chain, including crop monitoring, plant phenotyping, disease detection, soil and water assessment, postharvest quality evaluation, and food processing. It also encompasses emerging interdisciplinary approaches that integrate imaging technologies with precision agriculture, smart farming, and digital twins. Contributions addressing sustainability, resource efficiency, and green analytical methods are particularly encouraged. The purpose of this Special Issue is to provide a platform for cutting-edge research and reviews that demonstrate how imaging technologies can transform traditional agricultural practices into intelligent, automated, and sustainable systems. It aims to bridge the gap between engineering innovations and practical agricultural applications, facilitating the adoption of imaging tools in both research and industry. This Special Issue will complement existing literature by offering a consolidated and application-oriented perspective on imaging technologies in agriculture. While prior studies often focus on individual techniques or specific crops, this Special Issue will emphasize integrated approaches that combine imaging with AI, modeling, and non-destructive testing. Thus, it will advance the field by highlighting scalable solutions, cross-disciplinary innovations, and real-world implementations that support the transition toward precision and sustainable agriculture.
Dr. Saeid Jafari
Prof. Dr. Kuan-Chen Cheng
Dr. Yu Wen Ting
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. AgriEngineering is an international peer-reviewed open access monthly 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 1800 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
- artificial intelligence
- computer vision
- hyperspectral imaging
- non-destructive analysis
- precision agriculture
- smart farming
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