Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

被引:30
作者
Alexopoulos, Angelos [1 ]
Koutras, Konstantinos [1 ]
Ben Ali, Sihem [2 ]
Puccio, Stefano [3 ]
Carella, Alessandro [3 ]
Ottaviano, Roberta [4 ]
Kalogeras, Athanasios [1 ]
机构
[1] Athena Res Ctr, Ind Syst Inst, Patras 26504, Greece
[2] Assoc Sauvegarde Matmata, Matmata 6070, Tunisia
[3] Univ Palermo, Dept Agr Food & Forest Sci, I-90133 Palermo, Italy
[4] FgTech, Via san Rocco 5, I-40122 Bologna, Italy
来源
AGRONOMY-BASEL | 2023年 / 13卷 / 07期
关键词
UAV; IoT; satellite; Precision Agriculture; proximal sensing; remote sensing; challenges; UNMANNED AIRCRAFT SYSTEMS; SITE-SPECIFIC MANAGEMENT; CROP DISEASE DETECTION; SPATIAL-RESOLUTION; DATA FUSION; THINGS IOT; BIG DATA; INTERNET; TECHNOLOGIES; VEGETATION;
D O I
10.3390/agronomy13071942
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0.
引用
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页数:32
相关论文
共 243 条
[121]   From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges [J].
Liu, Ye ;
Ma, Xiaoyuan ;
Shu, Lei ;
Hancke, Gerhard Petrus ;
Abu-Mahfouz, Adnan M. .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2021, 17 (06) :4322-4334
[122]   A Wireless Sensor Network Deployment for Soil Moisture Monitoring in Precision Agriculture [J].
Lloret, Jaime ;
Sendra, Sandra ;
Garcia, Laura ;
Jimenez, Jose M. .
SENSORS, 2021, 21 (21)
[123]   Leaf nutrient spatial variability and site-specific fertilization maps within olive (Olea europaea L.) orchards [J].
López-Granados, F ;
Jurado-Expósito, M ;
Alamo, S ;
García-Torres, L .
EUROPEAN JOURNAL OF AGRONOMY, 2004, 21 (02) :209-222
[124]   Continuous Delivery of Customized SaaS Edge Applications in Highly Distributed IoT Systems [J].
Lopez-Viana, Ramon ;
Diaz, Jessica ;
Hernandez Diaz, Vicente ;
Martinez, Jose-Fernan .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (10) :10189-10199
[125]   Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements [J].
Lu, Saihong ;
Xuan, Junjie ;
Zhang, Tong ;
Bai, Xueer ;
Tian, Fei ;
Ortega-Farias, Samuel .
REMOTE SENSING, 2022, 14 (09)
[126]   Multispectral remote sensing for accurate acquisition of rice phenotypes: Impacts of radiometric calibration and unmanned aerial vehicle flying altitudes [J].
Luo, Shanjun ;
Jiang, Xueqin ;
Yang, Kaili ;
Li, Yuanjin ;
Fang, Shenghui .
FRONTIERS IN PLANT SCIENCE, 2022, 13
[127]   Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning [J].
Maimaitijiang, Maitiniyazi ;
Sagan, Vasit ;
Sidike, Paheding ;
Daloye, Ahmad M. ;
Erkbol, Hasanjan ;
Fritschi, Felix B. .
REMOTE SENSING, 2020, 12 (09)
[128]  
Maltamo M, 2014, MANAG FOR ECOSYST, V27, P1, DOI 10.1007/978-94-017-8663-8
[129]   On the Use of Unmanned Aerial Systems for Environmental Monitoring [J].
Manfreda, Salvatore ;
McCabe, Matthew F. ;
Miller, Pauline E. ;
Lucas, Richard ;
Madrigal, Victor Pajuelo ;
Mallinis, Giorgos ;
Ben Dor, Eyal ;
Helman, David ;
Estes, Lyndon ;
Ciraolo, Giuseppe ;
Mullerova, Jana ;
Tauro, Flavia ;
de Lima, M. Isabel ;
de Lima, Jao L. M. P. ;
Maltese, Antonino ;
Frances, Felix ;
Caylor, Kelly ;
Kohv, Marko ;
Perks, Matthew ;
Ruiz-Perez, Guiomar ;
Su, Zhongbo ;
Vico, Giulia ;
Toth, Brigitta .
REMOTE SENSING, 2018, 10 (04)
[130]   Applications of IoT for optimized greenhouse environment and resources management [J].
Maraveas, C. ;
Piromalis, D. ;
Arvanitis, K. G. ;
Bartzanas, T. ;
Loukatos, D. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 198