Special Issue "Machine Learning Applications for High-Throughput Phenotyping of Soil–Crop Interactions"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Eli Argaman
E-Mail Website
Guest Editor
Soil Erosion Research Station, Soil Conservation and Drainage Division, Ministry of Agriculture & Rural Development, Rishon Lezion, Israel
Interests: soil erosion; remote sensing; watershed management; erosion control
Special Issues and Collections in MDPI journals
Dr. Roi Ben-David
E-Mail Website
Guest Editor
Soil-Plant Interactions, Department of Vegetables and Field crops, Institute of Plant Sciences, ARO-Volcani Center, HaMaccabim Road, POB 15159, Rishon LeZion 7528809, Israel
Interests: plant breeding; plant genetics; plant physiology; molecular biology; genetics; genetic diversity; plant biotechnology; genomics; sequencing; PCR
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The current challenge of enhancing crop production necessitates a better understanding of soil–crop interactions and improved phenotypic investigation tools. Concurrently, there is a vast development in new cutting-edge machine learning algorithms and technologies for mining remotely sensed data that provide novel pathways toward the cost-effective high-throughput monitoring of crop plant on-farm soil.

The development of new data mining techniques enables innovative methods of extensive and vast dataset analysis, relying on remote sensing platforms, utilizing multi-scale and multi-band technologies for monitoring crop development across a growing season.

Despite this progress, relatively few studies have investigated these novel techniques, which can generate quantitative and qualitative state-of-the-art tools and assess spatiotemporal soil–crop interactions.

We are looking for innovative research studies that integrate remote-sensing-based machine learning and data mining approaches, encompassing the spectral and morphological high-throughput investigation of relevant field crop traits. Priority will be given to traits related to on-field crop yield, with an expected contribution to the long-term goal of optimizing crop production efficiency. Attention will also be given to land susceptibility issues driven by agronomic practices and the direct impact of soil structure and quality on crop production.

This Issue will collect insights and identify challenges and opportunities to develop new practices and methods to assess crop traits and soil–plant interaction efficiency.

Dr. Eli Argaman
Dr. Roi Ben-David
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 papers will be 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 2400 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 traits
  • Yield management
  • Remote sensing
  • Machine learning
  • Data mining

Published Papers

This special issue is now open for submission.
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