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: closed (31 March 2022) | Viewed by 588
Special Issue Editors
Interests: soil erosion; remote sensing; watershed management; erosion control
Special Issues, Collections and Topics in MDPI journals
Interests: plant breeding; plant genetics; plant physiology; molecular biology; genetics; genetic diversity; plant biotechnology; genomics; sequencing; PCR
Special Issues, Collections and Topics 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
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Keywords
- Crop traits
- Yield management
- Remote sensing
- Machine learning
- Data mining
