Advanced Spectral Remote Sensing for Smart Crop Monitoring in Agriculture and Horticulture
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 63
Special Issue Editors
Interests: agricultural remote sensing; crop phenotyping; unmanned aerial vehicle (UAV); hyperspectra
Interests: facilitated precise cultivation and quality control of chrysanthemums; ornamental plant germplasm resources and utilization; high yield and quality control of ornamental plant
Special Issue Information
Dear Colleagues,
Sustainable and precise crop production is critical to meeting global food demands, and spectral remote sensing has emerged as a transformative tool in smart agriculture and horticulture. By capturing reflectance data across multiple scales—from leaf and canopy levels to UAV and satellite platforms—researchers can monitor crop development with unprecedented accuracy. These technologies enable the assessment of key crop traits, including growth dynamics, nutrient status, pest and disease presence, and yield potential. Applications span diverse crops, from field staples like wheat and maize to high-value horticultural products such as fruits, vegetables, flowers, and protected crops. As smart farming evolves, integrating spectral sensing with advanced analytics is essential for optimizing productivity and resource efficiency.
This Special Issue seeks to address the challenges of real-time, non-destructive crop monitoring by advancing spectral remote sensing techniques. Despite technological progress, gaps remain in multi-scale data fusion, early stress detection, and predictive modeling for diverse cropping systems. We aim to compile cutting-edge research on spectral-based solutions that enhance precision agriculture, from field to horticultural applications. Contributions should explore novel sensing platforms (e.g., UAVs, satellites), machine learning algorithms, and scalable methods for actionable insights. By bridging theory and practice, this collection will support sustainable crop management and decision-making.
We invite original research, and case studies on spectral remote sensing for crop monitoring, including the following topics:
- Crop phenotyping and growth analysis using RGB, multispectral, hyperspectral, or LiDAR data.
- Nutrient and stress detection via spectral indicators for pests, diseases, and abiotic factors.
- Yield and quality prediction through machine/deep learning models.
- Multi-scale integration of UAV, ground-based, and satellite remote sensing.
- Applications in horticulture (e.g., fruits, vegetables, greenhouse crops) and field crops.
Studies leveraging AI for feature extraction, low-cost sensor solutions, or scalability in precision farming are particularly encouraged.
Dr. Jingshan Lu
Prof. Dr. Zhiyong Guan
Dr. Jie Zhu
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-blind 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
- spectral remote sensing
- crop phenotyping
- nutrient monitoring
- pest and disease detection
- yield and quality assessment
- UAV
- machine learning
- deep learning
- horticultural crops
- field crops
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