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
收藏
页码:377 / 384
页数:8
相关论文
共 59 条
[21]   3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications [J].
de Castro, Ana I. ;
Jimenez-Brenes, Francisco M. ;
Torres-Sanchez, Jorge ;
Pena, Jose M. ;
Borra-Serrano, Irene ;
Lopez-Granados, Francisca .
REMOTE SENSING, 2018, 10 (04)
[22]   UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras [J].
Deng, Lei ;
Mao, Zhihui ;
Li, Xiaojuan ;
Hu, Zhuowei ;
Duan, Fuzhou ;
Yan, Yanan .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2018, 146 :124-136
[23]   Weed detection in soybean crops using ConvNets [J].
Ferreira, Alessandro dos Santos ;
Freitas, Daniel Matte ;
da Silva, Gercina Goncalves ;
Pistori, Hemerson ;
Folhes, Marcelo Theophilo .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 143 :314-324
[24]  
Garre Parthasarathy, 2018, IOP Conference Series: Materials Science and Engineering, V455, DOI 10.1088/1757-899X/455/1/012030
[25]   Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach [J].
Han, Liang ;
Yang, Guijun ;
Yang, Hao ;
Xu, Bo ;
Li, Zhenhai ;
Yang, Xiaodong .
FRONTIERS IN PLANT SCIENCE, 2018, 9
[26]   A New Vegetation Segmentation Approach for Cropped Fields Based on Threshold Detection from Hue Histograms [J].
Hassanein, Mohamed ;
Lari, Zahra ;
El-Sheimy, Naser .
SENSORS, 2018, 18 (04)
[27]   UAV Low-Altitude Remote Sensing for Precision Weed Management [J].
Huang, Yanbo ;
Reddy, Krishna N. ;
Fletcher, Reginald S. ;
Pennington, Dean .
WEED TECHNOLOGY, 2018, 32 (01) :2-6
[28]   Monitoring nitrogen status of potatoes using small unmanned aerial vehicles [J].
Hunt, E. Raymond, Jr. ;
Horneck, Donald A. ;
Spinelli, Charles B. ;
Turner, Robert W. ;
Bruce, Alan E. ;
Gadler, Daniel J. ;
Brungardt, Joshua J. ;
Hamm, Philip B. .
PRECISION AGRICULTURE, 2018, 19 (02) :314-333
[29]   Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes [J].
Jung, Jinha ;
Maeda, Murilo ;
Chang, Anjin ;
Landivar, Juan ;
Yeom, Junho ;
McGinty, Joshua .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 152 :74-81
[30]   Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images [J].
Kerkech, Mohamed ;
Hafiane, Adel ;
Canals, Raphael .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 155 :237-243