Application of Hyperspectral 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: 10 April 2027 | Viewed by 412
Editor
Interests: hyperspectral imaging in agriculture; non-destructive evaluation of crop and seed quality; soil property assessment using spectral techniques; machine learning and deep learning for hyperspectral data analysis; sensing systems for precision and smart agriculture
Special Issue Information
Dear Colleagues,
The rapid development of hyperspectral imaging and sensing technologies has provided powerful tools for acquiring detailed spectral and spatial information from agricultural systems. Compared with conventional imaging or point-based spectral measurements, hyperspectral techniques enable non-destructive, high-throughput, and fine-scale characterization of crops, seeds, and soils, offering new opportunities for precision agriculture and smart farming.
We are pleased to invite you to contribute to this Special Issue, which brings together recent studies applying hyperspectral approaches to crop quality assessment, stress and damage detection, seed vigor evaluation, and soil property estimation. When combined with advanced data analysis methods, such as machine learning, deep learning, and sensor fusion, hyperspectral techniques allow for more accurate interpretation of complex spectral responses and improved prediction of key agronomic traits. Moreover, the integration of hyperspectral sensing with field phenotyping platforms, unmanned systems, and Internet of Things (IoT) technologies is accelerating the transition from laboratory-based research to real-world agricultural applications.
This Special Issue aims to provide a focused forum for recent advances in the application of hyperspectral imaging and sensing technologies in smart agriculture. The scope of the issue covers both methodological developments and practical applications, with particular emphasis on crop and soil quality evaluation, non-destructive testing, and data-driven modeling approaches that support precision management and decision-making in agriculture. The topic is closely aligned with the journal’s scope in agricultural engineering and emerging sensing technologies.
In this Special Issue, original research articles and review papers are welcome. Topics of interest include, but are not limited to, the following:
- Hyperspectral imaging for crop quality, stress, and disease detection.
- Non-destructive evaluation of seed vigor, aging, and physiological status.
- Hyperspectral methods for soil property estimation and soil quality assessment.
- Spectral data preprocessing, feature extraction, and band selection strategies.
- Machine learning and deep learning methods for hyperspectral agricultural data.
- Fusion of hyperspectral data with other sensors (RGB, thermal, LiDAR, and IoT).
- Field and laboratory hyperspectral phenotyping platforms.
- Practical applications of hyperspectral sensing in precision and smart agriculture.
We believe that this Special Issue will contribute to a deeper understanding of how hyperspectral technologies can be effectively applied to agricultural systems and will promote the exchange of ideas between researchers working on sensing technologies, data analysis, and agricultural applications.
We look forward to receiving your valuable contributions.
Prof. Dr. Kezhu Tan
Guest Editor
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-blind 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
- hyperspectral imaging
- smart agriculture
- crop quality assessment
- soil properties
- non-destructive testing
- precision agriculture
- machine learning
- deep learning
- sensor fusion
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