Unbemannter Luftfahrzeuge (UAV): technische Anwendungen, standardisierter Workflow, und zukünftige Entwicklungen in der Maisproduktion - Erkennung von Wasserstress, Unkrautkartierung, Überwachung der Nährstoffbilanz und Ertragsvorhersage

  • Xiuhao Quan
  • Reiner Doluschitz

Abstract

Als Folge der rasch fortschreitenden technologischen Entwicklungen und deren zunehmender Integration in die landwirtschaftliche Mechanisierung und Agrarsektor bezogene künstliche Intelligenz beginnen UAVs allmählich eine immer wichtigere Rolle vor allem bei der Dokumentation und Überwachung von Ackerkulturen zu spielen. Dieser Literaturüberblick stellt die Entwicklung von UAVs in vier dominanten Hauptanwendungen im Maisanbau vor und umfasst folgende Themen: (i) Erkennung von Wasserstress, (ii) Unkrautkartierung, (iii) Überwachung der Nährstoffbilanz und (iv) Ertragsvorhersage. Darüber hinaus fasst diese Arbeit die Methoden des UAV-Datenmanagements zusammen, erklärt, wie Expertensysteme in UAV-Systemen funktionieren und liefert standardisierte Workflow-Daten für Landwirte im Maisanbau. Weiterhin werden die Stärken, Schwächen, Chancen und Risiken des UAV-Einsatzes im Maisanbau analysiert. Auf der Grundlage von mehr als achtzig Publikationen und unserer eigenen Forschung weisen die Diskussion und die Schlussfolgerungen auf Schlüsselfragen des UAV-Einsatzes im Maisanbau und auf Forschungslücken hin, die geschlossen werden müssen, sowie auf eine Reihe von Empfehlungen für die Entwicklung von UAVs im Maisanbau in der Zukunft.

Literaturhinweise

Andrews, J. (2017): Six budget GPS guidance system options for farmers. https://www.fwi.co.uk/machinery/6-budget-gps-guidance-system-options-for-farmers, accessed on 5 June 2020

Barbedo, J. G. A. (2019): A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses. Drones 3, 40. https://doi.org/10.3390/drones3020040

Boon, M. A.; Drijfhout, A. P.; Tesfamichael, S. (2017): Comparison of a fixed-wing and multi-rotor uav for environmental mapping applications: a case study. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci. XLII-2/W6, 47–54. https://doi.org/10.5194/isprs-archives-XLII-2-W6-47-2017

Boursianis, A. D.; Papadopoulou, M. S.; Diamantoulakis, P.; Liopa-Tsakalidi, A.; Barouchas, P.; Salahas, G.; Karagiannidis, G.; Wan, S.; Goudos, S. K. (2020): Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things 100187. https://doi.org/10.1016/j.iot.2020.100187

Brauns, B.; Jakobsen, R.; Song, X.; Bjerg, P. L. (2018): Pesticide use in the wheat-maize double cropping systems of the North China Plain: Assessment, field study, and implications. Science of The Total Environment 616–617, pp. 1307–1316. https://doi.org/10.1016/j.scitotenv.2017.10.187

Burgos-Artizzu, X. P.; Ribeiro, A.; Guijarro, M.; Pajares, G. (2011): Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture 75, pp. 337–346. https://doi.org/10.1016/j.compag.2010.12.011

Burlacu, G.; Costa, R.; Sarraipa, J.; Jardim-Golcalves, R.; Popescu, D. (2014): A Conceptual Model of Farm Management Information System for Decision Support, in: Camarinha-Matos, L.M., Barrento, N. S.; Mendonça, R. (Eds.), Technological Innovation for Collective Awareness Systems, IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 47–54. https://doi.org/10.1007/978-3-642-54734-8_6

Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. (2015): Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images. Remote Sensing 7, pp. 4026–4047. https://doi.org/10.3390/rs70404026

Casa, R.; Pascucci, S.; Pignatti, S.; Palombo, A.; Nanni, U.; Harfouche, A.; Laura, L.; Di Rocco, M.; Fantozzi, P. (2019): UAV-based hyperspectral imaging for weed discrimination in maize. Precision agriculture 19. https://doi.org/10.3920/978-90-8686-888-9_45

Castaldi, F.; Pelosi, F.; Pascucci, S.; Casa, R. (2017): Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agric 18, pp. 76–94. https://doi.org/10.1007/s11119-016-9468-3

Chikoye, D.; Udensi, U. E.; Fontem Lum, A. (2005): Evaluation of a new formulation of atrazine and metolachlor mixture for weed control in maize in Nigeria. Crop Protection 24, pp. 1016–1020. https://doi.org/10.1016/j.cropro.2005.02.011

Cilia, C.; Panigada, C.; Rossini, M.; Meroni, M.; Busetto, L.; Amaducci, S.; Boschetti, M.; Picchi, V.; Colombo, R. (2014): Nitrogen Status Assessment for Variable Rate Fertilization in Maize through Hyperspectral Imagery. Remote Sensing 6, pp. 6549–6565. https://doi.org/10.3390/rs6076549

Corti, M.; Cavalli, D.; Cabassi, G.; Vigoni, A.; Degano, L.; Gallina, P. M. (2018): Application of a low?cost camera on a UAV to estimate maize nitrogen?related variables. Precision Agriculture pp. 675–696. https://doi.org/10.1007/s11119-018-9609-y

Cui, Z.; Chen, X.; Miao, Y.; Zhang, F.; Sun, Q.; Schroder, J.; Zhang, H.; Li, J.; Shi, L.; Xu, J.; Ye, Y.; Liu, C.; Yang, Z.; Zhang, Q.; Huang, S.; Bao, D. (2008): On-Farm Evaluation of the Improved Soil N–based Nitrogen Management for Summer Maize in North China Plain. Agronomy Journal 100, p. 517. https://doi.org/10.2134/agronj2007.0194

Directive (2009): Directive 2009/128/EC on the sustainable use of pesticides, 2009. 16. October 2018. https://www.europarl.europa.eu/RegData/etudes/STUD/2018/627113/EPRS_STU(2018)627113_EN.pdf, accessed on 5 June 2020

DJI (2020): Comprehensive Solution and Intelligent Operation. [WWW Document]. AGRAS T16. https://www.dji.com/t16?site=brandsite&from=nav, accessed on 5 June 2020

Fang, Q. X.; Ma, L.; Green, T. R.; Yu, Q.; Wang, T. D.; Ahuja, L. R. (2010): Water resources and water use efficiency in the North China Plain: Current status and agronomic management options. Agricultural Water Management 97, pp. 1102–1116. https://doi.org/10.1016/j.agwat.2010.01.008

Gabriel, J. L.; Zarco-Tejada, P. J.; López-Herrera, P. J.; Pérez-Martín, E.; Alonso-Ayuso, M.; Quemada, M. (2017): Airborne and ground level sensors for monitoring nitrogen status in a maize crop. Biosystems Engineering 160, pp. 124–133. https://doi.org/10.1016/j.biosystemseng.2017.06.003

Geipel, J.; Link, J.; Claupein, W. (2014): Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System. Remote Sensing 6, pp. 10335–10355. https://doi.org/10.3390/rs61110335

Hamuda, E.; Glavin, M.; Jones, E. (2016): A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture 125, pp. 184–199. https://doi.org/10.1016/j.compag.2016.04.024

Han, L.; Yang, G.; Dai, H.; Bo, X.; Yang, H. (2019): Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods 15, 10 (2019). https://doi.org/10.1186/s13007-019-0394-z

Herrmann, I.; Bdolach, E. (2019): Assessment of maize yield and phenology by drone?mounted superspectral camera. Precision Agriculture 21, pp. 51–76. https://doi.org/10.1007/s11119-019-09659-5

Ihuoma, S. O.; Madramootoo, C. A. (2017): Recent advances in crop water stress detection. Computers and Electronics in Agriculture 141, pp. 267–275. https://doi.org/10.1016/j.compag.2017.07.026

Jeffries, G. R.; Griffin, T. S.; Fleisher, D. H.; Naumova, E. N.; Koch, M.; Wardlow, B. D. (2020): Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning. Precision Agric 21, pp. 678–694. https://doi.org/10.1007/s11119-019-09689-z

Jha, K.; Doshi, A.; Patel, P.; Shah, M. (2019): A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 2, pp. 1–12. https://doi.org/10.1016/j.aiia.2019.05.004

Kaimaris, D.; Patias, P.; Sifnaiou, M. (2017): UAV and the comparison of image processing software. International Journal of Intelligent Unmanned Systems, ISSN: 2049-6427

Katsvairo, T. W.; Cox, W. J.; Van Es, H. M. (2003): Spatial Growth and Nitrogen Uptake Variability of Corn at Two Nitrogen Levels. Agron. J. 95, pp. 1000–1011. https://doi.org/10.2134/agronj2003.1000

Kenneth, C.; Chinecherem, O. (2018): A Review of Expert Systems in Agriculture. International Journal of Computer Science and Information Security (IJCSIS) 16(4), pp. 126-129

Krienke, B.; Ferguson, R. B.; Schlemmer, M.; Holland, K.; Marx, D.; Eskridge, K. (2017): Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor. Precision Agric 18, pp. 900–915. https://doi.org/10.1007/s11119-017-9534-5

Li, W.; Niu, Z.; Chen, H.; Li, D.; Wu, M.; Zhao, W. (2016): Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecological Indicators 67, pp. 637–648. https://doi.org/10.1016/j.ecolind.2016.03.036

Maresma, Á.; Ariza, M.; Martínez, E.; Lloveras, J.; Martínez-Casasnovas, J. (2016):. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service. Remote Sensing 8, 973. https://doi.org/10.3390/rs8120973

Michels, M.; Fecke, W.; Feil, J.; Musshoff, O.; Pigisch, J.; Krone, S. (2019): Smartphone adoption and use in agriculture empirical evidence from Germany. Precision Agriculture. https://doi.org/10.1007/s11119-019-09675-5

Mink, R.; Dutta, A.; Peteinatos, G.; Sökefeld, M.; Engels, J.; Hahn, M.; Gerhards, R. (2018):. Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping. Agriculture 8, p. 65. https://doi.org/10.3390/agriculture8050065

Murakami, E.; Saraiva, A. M.; Ribeiro, L. C. M.; Cugnasca, C. E.; Hirakawa, A. R.; Correa, P. L. P. (2007): An infrastructure for the development of distributed service-oriented information systems for precision agriculture. Computers and Electronics in Agriculture 58, pp. 37–48. https://doi.org/10.1016/j.compag.2006.12.010

Norasma, C.Y.N., Fadzilah, M.A., Roslin, N.A., Zanariah, Z.W.N., Tarmidi, Z., Candra, F.S., 2019. Unmanned Aerial Vehicle Applications In Agriculture. IOP Conf. Ser.: Mater. Sci. Eng. p. 506, 012063. https://doi.org/10.1088/1757-899X/506/1/012063

Oerke, E.-C. (2006): Crop losses to pests 13. Journal of Agricultural Science (2006), 144, pp. 31–43. https://doi.org/10.1017/S0021859605005708

Orakwe, L. C.; Okoye, N. M. (2016): Advanced Decision Support and Data Acquisition Systems for Field Irrigation Management: A Review. International Journal of Advanced Engineering and Management Research, Vol.1 Issue 1, pp. 78-93

Park, S.; Ryu, D.; Fuentes, S.; Chung, H.; Hernández-Montes, E.; O’Connell, M. (2017): Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sensing 9, p. 828. https://doi.org/10.3390/rs9080828

Pedersen, S. M.; Lind, K. M. (2017): Precision Agriculture: Technology and Economic Perspectives, Progress in Precision Agriculture. Springer International Publishing. https://doi.org/10.1007/978-3-319-68715-5

Pelosi, F.; Castaldi, F.; Casa, R. (2015): Operational unmanned aerial vehicle assisted post-emergence herbicide patch spraying in maize: a field study. In: Stafford, J.V. (Ed.), Precision Agriculture ’15. Wageningen Academic Publishers, The Netherlands, pp. 159–166. https://doi.org/10.3920/978-90-8686-814-8_19

Peña, J. M.; Torres-Sánchez, J.; de Castro, A. I.; Kelly, M.; López-Granados, F. (2013): Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images. PLOS ONE 8, e77151. https://doi.org/10.1371/journal.pone.0077151

Peña, J. M.; Torres-Sánchez, J.; de Castro, A. I.; Serrano-Pérez, A.; López-Granados, F. (2014): Comparing visible and color-infrared UAV imagery for early-season weed mapping the case of maize as a wide row crop. RHEA-2014. Second International Conference on Robotics and associated High-technologies and Equipment for Agriculture and forestry: new trends in mobile robotics, perception and actuation for agriculture and forestry, pp. 319-327. http://hdl.handle.net/10261/160279

Peña-Barragán, J. M.; Kelly, M. (2012): Object-based Approach for Crop Row Characterization in UAV Images for Site-specific Weed Management. Proceedings of the 4th GEOBIA, May 7-9, 2012 - Rio de Janeiro - Brazil. p. 426

Ping, J. L.; Dobermann, A. (2005): Processing of Yield Map Data. Precision Agric 6, pp. 193–212. https://doi.org/10.1007/s11119-005-1035-2

Poblete, T.; Ortega-Farías, S.; Ryu, D. (2018): Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard. Sensors 18, p. 397. https://doi.org/10.3390/s18020397

Prasad, G. N. R.; Babu, D. A. V. (2006): A Study on Various Expert Systems in Agriculture. Georgian Electronic Scientific Journal: Computer Science and Telecommunications 4(11), pp. 81-86

PrecisionHawk (2020): Deploy a Turnkey Drone Mapping and Analytics Solution for Agriculture. For Agriculture-count crops, quantify plant health and maximize yield. https://www.precisionhawk.com/agriculture, accessed on 30 November 2020

Prince Czarnecki, J. M.; Samiappan, S.; Wasson, L.; McCurdy, J. D.; Reynolds, D. B.; Williams, W. P.; Moorhead, R. J. (2017): Applications of Unmanned Aerial Vehicles in Weed Science. Advances in Animal Biosciences 8, pp. 807–811. https://doi.org/10.1017/S2040470017001339

Quemada, M.; Gabriel, J.; Zarco-Tejada, P. (2014): Airborne Hyperspectral Images and Ground-Level Optical Sensors as Assessment Tools for Maize Nitrogen Fertilization. Remote Sensing 6, pp. 2940–2962. https://doi.org/10.3390/rs6042940

Radoglou-Grammatikis, P.; Sarigiannidis, P.; Lagkas, T.; Moscholios, I. (2020): A compilation of UAV applications for precision agriculture. Computer Networks 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148

Rani, P. M. N.; Rajesh, T.; Saravanan, R. (2011): Expert Systems in Agriculture: A Review 14. Journal of Computer Science and Applications 3(1), pp. 59-71

Reger, M.; Bauerdick, J.; Bernhardt, H. (2018): Drones in Agriculture: Current and future legal status in Germany, the EU, the USA and Japan. Landtechnik 73(3), pp. 62–80. https://doi.org/10.15150/LT.2018.3183

Rhezali, A.; Lahlali, R. (2017): Nitrogen (N) Mineral Nutrition and Imaging Sensors for Determining N Status and Requirements of Maize. J. Imaging 3, 51. https://doi.org/10.3390/jimaging3040051

Safdar, M. E.; Tanveer, A.; Khaliq, A.; Riaz, M. A. (2015): Yield losses in maize (Zea mays) infested with parthenium weed (Parthenium hysterophorus L.). Crop Protection 70, pp. 77–82. https://doi.org/10.1016/j.cropro.2015.01.010

Saiz-Rubio, V.; Rovira-Más, F. (2020): From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management. Agronomy 10, 207. https://doi.org/10.3390/agronomy10020207

Shi, X.; Han, W.; Zhao, T.; Tang, J. (2019): Decision Support System for Variable Rate Irrigation Based on UAV Multispectral Remote Sensing. Sensors 19, 2880. https://doi.org/10.3390/s19132880

Sinha, J. P.; Kushwaha, H. L.; Kushwaha, D.; Singh, N.; Purushottam, M. (2016): Prospect of Unmanned Aerial Vehicle (UAV) Technology for Agricultural Production Management. International Conference on Emerging Technologies in Agricultural and Food Engineering 27 – 30th December 2016, Agricultural and Food Engineering Department, IIT Kharagpur

Sørensen, C. G.; Fountas, S.; Nash, E.; Pesonen, L.; Bochtis, D.; Pedersen, S. M.; Basso, B.; Blackmore, S. B. (2010): Conceptual model of a future farm management information system. Computers and Electronics in Agriculture 72, pp. 37–47. https://doi.org/10.1016/j.compag.2010.02.003

Sørensen, C. G.; Pesonen, L.; Bochtis, D. D.; Vougioukas, S. G.; Suomi, P. (2011): Functional requirements for a future farm management information system. Computers and Electronics in Agriculture 76, pp. 266–276. https://doi.org/10.1016/j.compag.2011.02.005

Srivastava, K.; Pandey, P. C.; Sharma, J. K. (2020): An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs. Drones 4, 58. https://doi.org/10.3390/drones4030058

Sylvester, G. (2018): E-agriculture in action: drones for agriculture. Food and Agriculture Organization of the United Nations, International Telecommunication Union, 2018. ISBN 978-92-5-130246-0s. http://www.fao.org/3/I8494EN/i8494en.pdf, accessed on 5 June 2020

Talaviya, T.; Shah, D.; Patel, N.; Yagnik, H.; Shah, M. (2020): Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture 4, pp. 58–73. https://doi.org/10.1016/j.aiia.2020.04.002

Tamirat, T. W.; Pedersen, S. M.; Lind, K. M. (2018): Farm and operator characteristics affecting adoption of precision agriculture in Denmark and Germany. Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 68, pp. 349–357. https://doi.org/10.1080/09064710.2017.1402949

Tsouros, D. C.; Bibi, S.; Sarigiannidis, P. G. (2019a): A Review on UAV-Based Applications for Precision Agriculture. Information 10, 349. https://doi.org/10.3390/info10110349

Tsouros, D. C.; Triantafyllou, A.; Bibi, S.; Sarigannidis, P. G. (2019b): Data Acquisition and Analysis Methods in UAV- based Applications for Precision Agriculture. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). Presented at the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), IEEE, Santorini Island, Greece, pp. 377–384. https://doi.org/10.1109/DCOSS.2019.00080

Usman, A.; Elemo, K. A.; Bala, A.; Umar, A. (2001): Effect of weed interference and nitrogen on yields of a maize/rice intercrop. International Journal of Pest Management 47, pp. 241–246. https://doi.org/10.1080/09670870110044625

Vergara-Díaz, O.; Zaman-Allah, M. A.; Masuka, B.; Hornero, A.; Zarco-Tejada, P.; Prasanna, B. M.; Cairns, J. E.; Araus, J. L. (2016):. A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Front. Plant Sci. 7. https://doi.org/10.3389/fpls.2016.00666

Wahab, I.; Hall, O.; Jirström, M. (2018): Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa. Drones 2, 28. https://doi.org/10.3390/drones2030028

Wang, X.; Zhang, R.; Song, W.; Han, L.; Liu, X.; Sun, X.; Luo, M.; Chen, K.; Zhang, Y.; Yang, H.; Yang, G.; Zhao, Y.; Zhao, J. (2019): Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV). Sci Rep 9, 3458. https://doi.org/10.1038/s41598-019-39448-z

Wiseman, L.; Sanderson, J.; Zhang, A.; Jakku, E. (2019): Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS - Wageningen Journal of Life Sciences 90–91, 100301. https://doi.org/10.1016/j.njas.2019.04.007

Wu, G.; Miller, N. D.; de Leon, N.; Kaeppler, S. M., Spalding, E. P. (2019): Predicting Zea mays Flowering Time, Yield, and Kernel Dimensions by Analyzing Aerial Images. Front. Plant Sci. 10, 1251. https://doi.org/10.3389/fpls.2019.01251

XAG, 2020. XSAS intelligent agriculture systems [WWW Document]. XSAS intelligent agriculture systems. URL https://www.xa.com/xsas, accessed on 5 June 2020

Yang, S.; Yang, X.; Mo, J. (2018): The application of unmanned aircraft systems to plant protection in China. Precision Agric 19, pp. 278–292. https://doi.org/10.1007/s11119-017-9516-7

Yin, X.; Jaja, N.; McClure, M. A. M.; Hayes, R. (2011a): Comparison of Models in Assessing Relationship of Corn Yield with Plant Height Measured during Early- to Mid-Season. JAS 3, p. 14. https://doi.org/10.5539/jas.v3n3p14

Yin, X.; McClure, M. A.; Jaja, N.; Tyler, D. D.; Hayes, R. M. (2011b): In-Season Prediction of Corn Yield Using Plant Height under Major Production Systems. Agron. J. 103, pp. 923–929. https://doi.org/10.2134/agronj2010.0450

Zhai, Z.; Martínez, J. F.; Beltran, V.; Martínez, N. L. (2020): Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture 170, 105256. https://doi.org/10.1016/j.compag.2020.105256

Zhang, C.; Kovacs, J. M. (2012): The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric 13, pp. 693–712. https://doi.org/10.1007/s11119-012-9274-5

Zhang, L.; Niu, Y.; Zhang, H.; Han, W.; Li, G.; Tang, J.; Peng, X. (2019a): Maize Canopy Temperature Extracted from UAV Thermal and RGB Imagery and Its Application in Water Stress Monitoring. Front. Plant Sci. 10, 1270. https://doi.org/10.3389/fpls.2019.01270

Zhang, L.; Zhang, H.; Niu, Y.;Han, W. (2019b): Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing. Remote Sensing 11, 605. https://doi.org/10.3390/rs11060605

Zhang, M.; Zhou, J.; Sudduth, K. A.; Kitchen, N. R. (2020): Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. Biosystems Engineering 189, pp. 24–35. https://doi.org/10.1016/j.biosystemseng.2019.11.001

Zhu, W.; Sun, Z.; Peng, J.; Huang, Y.; Li, J.; Zhang, J.; Yang, B.; Liao, X. (2019): Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales. Remote Sensing 11, p. 2678. https://doi.org/10.3390/rs11222678

Veröffentlicht
2021-03-24
Zitationsvorschlag
Quan, X., & Doluschitz, R. (2021). Unbemannter Luftfahrzeuge (UAV): technische Anwendungen, standardisierter Workflow, und zukünftige Entwicklungen in der Maisproduktion - Erkennung von Wasserstress, Unkrautkartierung, Überwachung der Nährstoffbilanz und Ertragsvorhersage. LANDTECHNIK, 76(1). https://doi.org/10.15150/lt.2021.3263
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