Data acquisition and analysis methods in UAV-based applications for Precision Agriculture

被引：29

作者：

Tsouros, Dimosthenis C. ^{[1
]}

Triantafyllou, Anna ^{[1
]}

Bibi, Stamatia ^{[1
]}

Sarigannidis, Panagiotis G. ^{[1
]}

机构：

[1] Univ Western Macedonia, Dept Informat & Telecommun Engn, Kozani, Greece

来源：

2019 15TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (DCOSS) | 2019年

关键词：

Remote Sensing; IoT; UAV; UAS; Unmanned Aerial Vehicle; Unmanned Aerial System; Image processing; Precision Agriculture; Smart Farming; Review; VEGETATION INDEXES; SYSTEMS;

D O I：

10.1109/DCOSS.2019.00080

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Precision Agriculture enabling the acquisition of real-time environmental data. IoT devices like Unmanned Aerial Vehicles (UAVs) equipped with cameras, sensors, and GPS receivers can deliver a variety of IoT services and applications related to fields management, by capturing images from great heights. However, there are many issues to be resolved before the effective use of UAVs in the agriculture domain, including the data collection and processing methods. There is still no standardized workflow and processes for most UAV-based applications for Precision Agriculture. In this paper we summarize the data acquisition methods and technologies to acquire images in UAV-based Precision Agriculture and appoint the benefits and drawbacks of each one. We also review popular data analysis methods of remotely sensed imagery and discuss the outcomes of each method and its potential application in the farming operations.

引用

页码：377 / 384

页数：8

共 59 条

[51] Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa [J].

Wahab, Ibrahim ;

Hall, Ola ;

Jirstrom, Magnus .

DRONES, 2018, 2 (03) :1-16

[52] Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape [J].

Wan, Liang ;

Li, Yijian ;

Cen, Haiyan ;

Zhu, Jiangpeng ;

Yin, Wenxin ;

Wu, Weikang ;

Zhu, Hongyan ;

Sun, Dawei ;

Zhou, Weijun ;

He, Yong .

REMOTE SENSING, 2018, 10 (09)

[53] 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications [J].

Westoby, M. J. ;

Brasington, J. ;

Glasser, N. F. ;

Hambrey, M. J. ;

Reynolds, J. M. .

GEOMORPHOLOGY, 2012, 179 :300-314

[54] VEGETATION INDEXES IN CROP ASSESSMENTS [J].

WIEGAND, CL ;

RICHARDSON, AJ ;

ESCOBAR, DE ;

GERBERMANN, AH .

REMOTE SENSING OF ENVIRONMENT, 1991, 35 (2-3) :105-119

[55] Develop an unmanned aerial vehicle based automatic aerial spraying system [J].

Xue, Xinyu ;

Lan, Yubin ;

Sun, Zhu ;

Chang, Chun ;

Hoffmann, W. Clint .

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 128 :58-66

[56] Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives [J].

Yang, Guijun ;

Liu, Jiangang ;

Zhao, Chunjiang ;

Li, Zhenhong ;

Huang, Yanbo ;

Yu, Haiyang ;

Xu, Bo ;

Yang, Xiaodong ;

Zhu, Dongmei ;

Zhang, Xiaoyan ;

Zhang, Ruyang ;

Feng, Haikuan ;

Zhao, Xiaoqing ;

Li, Zhenhai ;

Li, Heli ;

Yang, Hao .

FRONTIERS IN PLANT SCIENCE, 2017, 8

[57] The application of unmanned aircraft systems to plant protection in China [J].

Yang, Shulin ;

Yang, Xiaobing ;

Mo, Jianyou .

PRECISION AGRICULTURE, 2018, 19 (02) :278-292

[58] The application of small unmanned aerial systems for precision agriculture: a review [J].

Zhang, Chunhua ;

Kovacs, John M. .

PRECISION AGRICULTURE, 2012, 13 (06) :693-712

[59] Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV [J].

Ziliani, Matteo G. ;

Parkes, Stephen D. ;

Hoteit, Ibrahim ;

McCabe, Matthew F. .

REMOTE SENSING, 2018, 10 (12)

← 1 2 3 4 5 6 →