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Special Issue "Advances in Agricultural Remote Sensing and Artificial Intelligence"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 3733
Special Issue Editors
Interests: remote sensing; agriculture; crop modeling; machine learning
Interests: agriculture remote sensing; spatial analysis; plant biosecurity
Special Issue Information
Agriculture plays a critical role in the global economy. With the continuing expansion of the human population, the pressure on the agricultural system is increasing faster than ever. Precise and frequent monitoring of agricultural health and productivity is now critical for food security and economic sustainability. When remote sensing is used as a tool to monitor agriculture, the analytics must be reliable and accurate. Machine learning technology has provided highly accurate solutions to geospatial problems for many years. The availability of data at multiple scales over large geographical areas has great potential to enable interesting methodologies and knowledge development in the agricultural domain. On the other hand, advanced machine learning has emerged as a powerful approach for analyzing remote sensing data. There is a growing trend to develop such an approach to assist in the digital transformation of agriculture, such as land use monitoring, crop yield forecasting and optimization, crop diseases and pest management, etc. The aim of this Special Issue is to disseminate the latest research findings in the machine learning methods for crops monitoring using remote sensing. This includes but is not limited to crop yield prediction, cropland area change, crop phenology, agricultural drought and water stress, crop classification, weeds detection, disease detection, yield estimation, plants counting, etc. Papers are required to include a novelty, such as a new satellite sensor or data archive, a new approach to analysis, or a novel application to improve crop monitoring and evaluation. I look forward to receiving your contributions.
Dr. Muhammad Moshiur Rahman
Prof. Dr. Andrew Robson
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 2500 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.
- crop growth and health condition
- crop phenology
- crop yield forecasting
- drought stress and irrigation
- machine learning
- image processing
- crop classification
- satellite remote sensing
- crop monitoring and mapping