气候变化领域集成服务门户(CCPortal): A Review of Deep Learning in Multiscale Agricultural Sensing

CCPortal

DOI	10.3390/rs14030559
	A Review of Deep Learning in Multiscale Agricultural Sensing
	Wang, Dashuai; Cao, Wujing; Zhang, Fan; Li, Zhuolin; Xu, Sheng; Wu, Xinyu
发表日期	2022
EISSN	2072-4292
卷号	14 期号:3
英文摘要	Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture.
英文关键词	precision agriculture; deep learning; convolutional neural networks; transfer learning; few-shot learning; remote sensing; proximal sensing
语种	英语
WOS研究方向	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000754955900001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281111
作者单位	Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, CAS; China Agricultural University; Zhejiang Sci-Tech University
推荐引用方式 GB/T 7714	Wang, Dashuai,Cao, Wujing,Zhang, Fan,et al. A Review of Deep Learning in Multiscale Agricultural Sensing[J],2022,14(3).
APA	Wang, Dashuai,Cao, Wujing,Zhang, Fan,Li, Zhuolin,Xu, Sheng,&Wu, Xinyu.(2022).A Review of Deep Learning in Multiscale Agricultural Sensing.REMOTE SENSING,14(3).
MLA	Wang, Dashuai,et al."A Review of Deep Learning in Multiscale Agricultural Sensing".REMOTE SENSING 14.3(2022).