Spectral Data Analytics for Crop Growth Information
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".
Deadline for manuscript submissions: 25 February 2026 | Viewed by 3
Special Issue Editors
Interests: plant phenomics; spectral imaging; plant physiology; plant pathology; smart agriculture; agricultural mechanization
Interests: plant phenomics; agricultural artificial intelligence; agricultural big data; precision agriculture; crop efficient production
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Crop spectral intelligent sensing and analytics represent a transformative force driving the development of digital agriculture. This technology enables high-throughput, non-destructive, and real-time monitoring of key growth parameters such as nutritional status, water dynamics, and stress responses by acquiring and interpreting spectral information from crops. The deep integration of artificial intelligence and machine learning methods has significantly enhanced the accuracy and efficiency of multi-trait crop phenotyping. This has not only substantially improved the capabilities of growth modeling, stress, and yield prediction, but also provides critical support for genotype–phenotype association analysis.
This Special Issue is dedicated to showcasing innovative research and integrated applications of spectral sensing technologies and AI algorithms in the field of intelligent crop growth monitoring. We warmly invite submissions from scholars across multiple disciplines, including agronomy, remote sensing, computer science, agricultural engineering, optical engineering, and biotechnology. Topics of interest include, but are not limited to, the following:
- Advanced Spectral Sensing Technologies: Development of novel indoor/field spectral sensors; and multi-platform (ground-based, airborne, satellite) and multi-scale (tissue, plant, canopy) spectral data acquisition, calibration, and fusion techniques.
- Intelligent Analytics Algorithms and Models: Innovation in spectral data modeling, feature mining, and parameter inversion algorithms based on AI methods such as deep learning and transfer learning; and integrated estimation of multiple crop indicators (nutrients, water, biomass, yield, etc.).
- Phenomics and Breeding Applications: Application of high-throughput spectral phenotyping in genotype–phenotype association studies; and spectral identification of crop resistance to biotic/abiotic stresses.
- Integrated Digital Agriculture Applications: Spectral sensing-based precision farming management (fertilization/irrigation); integrated growth modeling and yield prediction systems; and intelligent crop disease, pest diagnosis, and early warning technologies.
We welcome all types of submissions, including original research articles, reviews, and perspectives.
Dr. Xiaodong Zhang
Dr. Shijie Tian
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 100 words) can be sent to the Editorial Office for announcement on this website.
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. Agriculture 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
- crop monitoring
- spectral analysis
- artificial intelligence
- high-throughput phenotyping
- machine learning
- spectral sensors
- smart agriculture
- germplasm evaluation
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