Smart Agriculture for Crop Phenotyping
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 6
Special Issue Editors
Interests: remote sensing; precision agriculture; water use efficiency; smart irrigation; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Interests: crop stress monitoring; UAV; phenotyping; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Current projections indicate that the world population will increase from 6.9 billion to 9.1 billion by 2050. In the context of climate change and finite agricultural resources, maintaining and enhancing crop yields has become an increasingly formidable challenge. Against this backdrop, crop phenotyping has emerged as a vital field in modern agricultural research, offering innovative solutions to address these pressing issues.
This Special Issue is dedicated to exploring the latest advancements in smart agriculture technologies for crop phenotyping. We aim to integrate interdisciplinary innovations across artificial intelligence (AI), machine learning, hyperspectral imaging, drone-based remote sensing, and robotic automation. By enabling high-throughput, non-invasive phenotyping data collection and intelligent analytics, these cutting-edge technologies empower researchers to decode dynamic crop growth patterns, stress-resilience traits, and genotype-phenotype associations with unparalleled precision. This, in turn, accelerates crop breeding cycles and optimizes field management practices, offering a promising pathway to enhance agricultural productivity and sustainability in an era of global challenges.
We invite original scientific contributions that highlight the latest advancements in smart agriculture technologies for crop phenotyping. Topics of interest include, but are not limited to, the following:
- Development of multi-scale phenotyping systems using drones and ground robots to capture comprehensive and high-resolution data.
- Applications of machine and deep learning in extracting morphological, physiological, and biochemical traits under biotic and abiotic stresses, providing deeper insights into crop performance and resilience.
- Integration of high-throughput phenotyping platforms with AI-powered decision-support tools for precision agriculture, streamlining workflows from data collection to actionable insights.
- Techniques for plant mapping, feature extraction, and stress estimation using UAVs and other remote sensing data, enabling the real-time monitoring and assessment of crop health.
- Case studies showcasing the application of smart phenotyping for resource optimization (water, fertilizers), pest and disease monitoring, and climate adaptation strategies, demonstrating tangible benefits for sustainable agriculture.
Prof. Dr. Wenting Han
Dr. Liyuan Zhang
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. Agronomy 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 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
- artificial intelligence
- machine learning
- deep learning
- multi-scale phenotyping systems
- prescription maps
- site-specific crop management
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