Data-Driven Fields: AI and Unmanned Sensing Technologies in Agricultural Optimization
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 August 2026 | Viewed by 1794
Special Issue Editors
Interests: remote sensing; machine learning; precision agriculture; UAV; high-throughput phenotyping; hyperspectral imaging
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; robot in-situ/IoT; crop phenotyping; agricultural sensor integration
Interests: remote sensing; data fusion; precision agriculture; digital agriculture; carbon-water-crop nexus; global change
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue invites cutting-edge research at the intersection of artificial intelligence and sensor technology to accelerate the transition to digital, precise, and intelligent agriculture. High-resolution remote sensing, combined with deep learning and emerging foundation/generative models, is reshaping how we scout fields, diagnose crop stress, guide variable-rate inputs, and forecast yield. We particularly welcome studies that reduce data-acquisition costs through synthetic data and domain adaptation, fuse multi-modal sources (satellite, UAV, robot in-situ/IoT, agronomic text), and operationalize real-time decision support for irrigation, fertilization, and pest/disease management. Contributions that integrate physical knowledge with data-driven models, e.g., digital twins of fields and greenhouse, are encouraged.
This Special Issue focuses on, but is not limited to, agricultural engineering in the following areas:
- Multi-modal data fusion;
- Intelligent monitoring and field robotics;
- Crop disease and pest detection using deep learning and generated data;
- Sensors and detection technologies for precision agriculture;
- Muti-scale remote sensing and artificial intelligence in agriculture;
- Intelligent crop monitoring and management systems;
- Combination of physical knowledge with data-driven models in the agricultural digital twin systems;
- Real-time decision-making systems for digital agriculture.
Dr. Lang Qiao
Dr. Dehua Gao
Dr. Jiang Chen
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. 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
- digital agriculture
- crop phenotyping
- computer vision in agriculture
- drone
- sustainable agriculture
- sensors
- pests and diseases
- AI-generated content
- agricultural optimization
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