Special Issue "Crop Yield Estimation through Remote Sensing Data"
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 31 May 2023 | Viewed by 3391
Special Issue Editors

Interests: precision agriculture; application technology; remote sensing; unmanned aerial vehicle; data science and services; process modeling; optimization and control

Interests: machine learning; compressive sensing; computational imaging; radar and array signal processing; digital signal and image processing; remote sensing
Special Issues, Collections and Topics in MDPI journals

Interests: smart agriculture; artificial intelligence in agriculture; agricultural robotics and machinery; remote sensing; computer vision; machine/deep learning; data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleages,
Highly accurate and reliable crop yield estimation is critical for improved crop production process management and strategic planning. Remote sensing has been studied and developed for crop yield estimation. However, it is still being investigated with the aim of increasing the accuracy and reliability of crop yield estimation. This Special Issue aims to provide a perspective of the development and application of crop yield estimation through remote sensing from spaceborne, airborne and ground-based systems. Machine/deep learning has recently been brought in to increase the accuracy and reliability of crop yield estimation using remotely sensed data. This Special Issue invites authors to share their achievements on topics including but not limited to the following related to crop yield estimation through remote sensing: (1) at national or regional scale for crop production planning; (2) at farm or field scale for precision agriculture operations; (3) assimilation remote sensing data into crop models; (4) developing specialized machine/deep learning schemes and algorithms.
Dr. Yanbo Huang
Dr. Ali C. Gurbuz
Dr. Xin 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 2200 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
- crop yield estimation
- remote sensing
- machine learning