Drones and AI for Crop Information Sensing and Decision-Making Models
A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".
Deadline for manuscript submissions: 15 December 2026 | Viewed by 583
Special Issue Editors
Interests: smart agriculture; UAV; remote sensing; plant phenotype and disease–pest monitoring; crop yield prediction; deep learning; agricultural AI applications
Special Issues, Collections and Topics in MDPI journals
Interests: smart agriculture and forest; remote sensing; deep learning; agricultural AI applications
Special Issue Information
Dear Colleagues,
We are pleased to invite you to submit your manuscripts to the MDPI Drones Special Issue on “Drones+AI for Crop Information Sensing and Decision-Making Models”.
Artificial intelligence technology is increasingly applied in agricultural information sensing and decision-making, and drone remote sensing provides vast data support for agricultural AI applications. In recent years, the integration of low-altitude drones and AI has emerged as a cutting-edge direction in smart agriculture. This Special Issue focuses on crop information sensing and decision-making applications using low-altitude drones and AI. We aim to highlight the latest advancements, key technologies, instruments, and management systems in areas such as the monitoring of crop moisture, nutrient, disease, pest, height, biomass, 3D morphology, and yield, as well as the development of airborne sensors or cameras, water–fertilizer–pesticide operation management systems, and intelligent unmanned farm management systems. These innovations will support smart agriculture and sustainable agricultural development all over the world.
This Special Issue welcomes manuscripts related to the following themes:
- AI-driven crop moisture and nutrient monitoring using low-altitude drones.
- Drone-based AI detection and analysis of crop diseases and pests.
- Low-altitude sensing of crop height, biomass, and 3D morphology.
- AI-powered crop yield prediction and modeling from drone data.
- Th development of advanced airborne sensors and cameras for agricultural AI.
- Intelligent water–fertilizer–pesticide operation management systems.
- Unmanned farm management systems integrating low-altitude drones and AI.
- Decision-making algorithms for smart agriculture using drone-collected data.
Prof. Dr. Fei Liu
Dr. Wenwen Kong
Dr. Xiangyu Lu
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. Drones 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
- smart agriculture
- remote sensing
- UAV
- drones
- AI models
- deep learning
- plant phenotype
- crop water content
- crop nutrient
- crop disease
- crop pest
- crop yield
- weed detection
- 3D digital technology
- multi-spectral imaging sensors
- crop smart management system
- crop decision-making models
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