Deep Learning and Remote Sensing for Agriculture
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 (30 November 2020) | Viewed by 78763
Special Issue Editor
Interests: machine learning; computer vision; image processing; pattern recognition; remote sensing; application in agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, agriculture is facing important challenges to increase productivity and quality while reducing the environmental impact. Meeting these challenges, through advances in science and technology, has become the goal for many worldwide research teams. Sustainable crop production depends on innovation in many fields, such as agronomy, sensors, data science, robotics, biotechnology, etc. The last two decades have seen a growing trend towards application of remote sensing technologies to the agriculture domain. Indeed, modern remote sensing offers unprecedented possibilities for acquiring land images in an easy, flexible, and fast manner, making it possible to obtain interesting information on crops conditions. One major issue that has dominated the field of remote sensing for many years concerns automatic data processing, modeling, and analysis. On the other hand, the deep learning approach continues to show impressive performance in several areas, and existing research recognizes the essential role played by this approach to solve many hard problems related to data modeling, interpretation, classification, etc. The aim of this Special Issue is to disseminate the latest research findings in the deep learning methods for crops monitoring using remote sensing. It includes but is not limited to crops classification, weeds detection, disease detection, yield estimation, plants counting, etc.
Dr. Adel Hafiane
Guest Editor
Manuscript Submission Information
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Keywords
- precision agriculture
- environment
- aerial imaging
- signal processing
- image processing
- machine learning
- plant classification
- crop monitoring
- field mapping
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