全文截稿: 2021-10-01
影响因子: 4.65
中科院JCR分区:
• 大类 : 地球科学 - 2区
• 小类 : 遥感 - 2区
网址：
https://www.journals.elsevier.com/international-journal-of-applied-earth-observation-and-geoinformation
Use of high-resolution remote sensing technologies for precision agriculture has increased rapidly. Accurate, consistent, and reliable information of field conditions within the growing season optimizes field-level management for precision farming and ensures sustainable agricultural production and desirable environmental outcome. Recent advances in earth observation technology, especially with increasingly available, affordable, and compatible platforms (e.g., unmanned aerial vehicle - UAV) and sensors (e.g., lightweight multispectral, hyperspectral, thermal, and LiDAR), can acquire images with unprecedented high spatial, spectral, and temporal resolutions for precision agriculture practices. The data acquisition, processing, and analysis based on artificial intelligence (AI) and quantitative modeling present a certain degree of intelligence in precision farming, but still have numerous scientific and technical challenges in preprocessing, data extraction and synthesis, quantitative analysis, and information delivery due to multi-scale, multi-sensor and multi-platform, and multi-temporal earth observation. As such, novel research is required to develop improved image collection and transfer techniques, address a variety of issues related to image preprocessing and cross-sensor integration, streamline data processing for field-level plant condition retrieval, and implement artificial intelligence for decision support.
The focus of this special issue of the International Journal of Applied Earth Observation and Geoinformation is the advances in intelligent remote sensing for precision farming in the broad field of agricultural sensing, but emphasizing the applications of unmanned aerial vehicles (UAV) remotely sensed techniques and AI approaches (e.g., machine learning, deep learning) in precision farming. For this special issue, example topics include, but are not limited to:
plant and plantation-line detection or segmentation
plant growth status monitoring
estimation of crop nutrient composition
crop biomass and yield estimation
plant disease or pest/weed infestation monitoring
phenology and phenotype detection
real-time preprocessing and processing with UAV images
RGB, multispectral, and hyperspectral remote sensing
multi-scale learning
farming robots with GNSS
livestock-related investigations
cross-domain adaptations
field spectroscopy
