Crop Production Parameter 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: 30 September 2024 | Viewed by 3377
Special Issue Editors
Interests: aerial application technology (manned aircraft and unmanned aerial vehicles); remote sensing for precision application (space-borne, airborne, and ground truthing); machine learning, soft computing and decision support for precision agriculture; spatial statistics for remote sensing data analysis; image processing; process modeling; optimization; control and automation
Special Issues, Collections and Topics in MDPI journals
Interests: smart/digital agriculture; artificial intelligence in agriculture; crop prediction models; UAV/UGV swarm
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleague,
Highly accurate and reliable estimation of crop production parameters, such as biomass and yield, is critical for improved crop production process management and strategic planning. Remote sensing has been studied and developed for estimating plant biomass and crop yield. However, it is still being investigated for increasing the accuracy and reliability of the estimations. This Special Issue aims to provide a comprehensive view of the development and application of crop production parameter estimation using remote sensing from satellite, airborne, manned, and unmanned aerial vehicles to ground-based systems. In recent years, machine/deep learning has been developed and applied to increase the accuracy and reliability of crop production parameter estimation using remotely sensed data. This Special Issue wishes to explore the achievements in but does not limit itself to, the following scopes of crop production parameter estimation for biomass, yield, or any other related parameters using remote sensing: (1) at the national or regional scale for crop production planning; (2) at farm or field scale for precision agriculture operations; (3) assimilation of remote sensing data into crop models and (4) developing specialized machine/deep learning schemes and algorithms.
Dr. Yanbo Huang
Dr. Xin Zhang
Dr. Chandan Kumar
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
- crop production parameter estimation
- plant biomass
- crop yield
- remote sensing
- machine learning
Related Special Issue
- Crop Yield Estimation through Remote Sensing Data in Agronomy (4 articles)