Remote Sensing in Smart Agriculture
A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 36405
Special Issue Editor
Special Issue Information
Dear colleagues,
Smart agriculture is a new trend of agricultural development explored as a dividend of the modern information technology revolution. It is an advanced stage of agricultural development integrating intensive production, intelligent remote control, precision management, data analysis, and field operation. Smart agriculture plays an important role in improving resource utilization and land productivity, ensuring food security, achieving carbon neutrality, and promoting the Sustainable Development Goals of the United Nations.
Smart agriculture is the "ecological integration" and "gene recombination" of modern information technology and the whole industrial chain of agricultural production, operation, management, and service. Among them, satellite, UAV, and ground remote sensing technology play an important role in information support for the production and management of field planting. Remote sensing can provide growth, nutrient, pest, disease, and phenotype information for crops, fruit trees, tea trees, and others. It is useful for fertilizing, irrigating , pesticide spreading, and crop breeding to make accurate decisions.
In recent years, smart agriculture has become a hot spot in the agricultural development of various countries. Remote sensing technology provides high spatial and temporal monitoring information for the development of smart agriculture. Based on the above background, we wish to publish a Special Issue on the topic of field crops for international peers to discuss the latest remote sensing technology in the field of smart agriculture and jointly promote better application of remote sensing technology in agriculture.
Dr. Xingang Xu
Guest Editor
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Keywords
- crop
- nitrogen
- water stress
- pest and disease
- growth prediction
- yield
- vertical structure
- phenotyping
- deep learning
- machine learning
- hyperspectral
- LiDAR
- UAV
- smart agriculture.
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