Crop Yield Estimation Based on Remote Sensing and Artificial Intelligence
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 31 August 2024 | Viewed by 82
Special Issue Editors
Interests: agriculture; artificial intelligence; remote-sensing; robotic; landscape; ecosystems and LiDAR
Interests: agriculture; artificial intelligence; bio-engineering and biosystem engineering not elsewhere classified
Interests: agriculture; artificial intelligence; crop protection; remote-sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Yield predictions are crucial for enabling farmers to make informed decisions in the field. Particularly valuable are those predictions that can be made well in advance of the harvest. Yield predictions involve numerous parameters pertaining to plants (e.g., fruit size, area, type of crop), weather conditions, plant systems, pruning, among others. In recent years, artificial intelligence has played a significant role in yield predictions across extensive crops, orchard crops, and horticulture. Remote sensing technologies, such as LiDAR, satellite imagery, and multispectral and hyperspectral information, have become especially important. This information can be gathered using both terrestrial and aerial platforms.
This Special Issue aims to cover all the solutions proposed by researchers for estimating crop yields, with a focus on applications in real-world agriculture. Topics may span a broad range of studies, provided they involve the use of remote sensing and artificial intelligence. Examples of topics that could be considered include the following:
- Integration of AI and LiDAR technology for precision agriculture;
- Advancements in satellite imagery for crop monitoring and yield estimation;
- Utilizing multispectral and hyperspectral imaging in horticulture;
- Machine learning models for predicting weather impact on crop yields;
- AI-driven pest and disease detection systems for crop management;
- Optimization of irrigation systems using remote sensing data;
- Deep learning techniques for enhanced crop type classification;
- Predictive analytics for soil health and its impact on crop yields;
- Automated crop counting and size estimation using AI;
- Impact of climate change on crop yields: AI and remote sensing approaches.
Dr. Orly Enrique Apolo-Apolo
Dr. Simon Appeltans
Dr. Mino Sportelli
Dr. Nathaniel K. Newlands
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. Remote Sensing 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 2700 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
- data analytics
- remote sensing
- LiDAR
- spectral analysis
- crop modelling
- geospatial data
