Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-28T13:15:26.324Z Has data issue: false hasContentIssue false

Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control

Published online by Cambridge University Press:  01 June 2017

A. I. de Castro*
Affiliation:
Institute for Sustainable Agriculture -CSIC. Apdo 4048, 14080 Córdoba (Spain)
J. M. Peña
Affiliation:
Institute for Sustainable Agriculture -CSIC. Apdo 4048, 14080 Córdoba (Spain)
J. Torres-Sánchez
Affiliation:
Institute for Sustainable Agriculture -CSIC. Apdo 4048, 14080 Córdoba (Spain)
F. Jiménez-Brenes
Affiliation:
Institute for Sustainable Agriculture -CSIC. Apdo 4048, 14080 Córdoba (Spain)
F. López-Granados
Affiliation:
Institute for Sustainable Agriculture -CSIC. Apdo 4048, 14080 Córdoba (Spain)
Get access

Abstract

In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.

Type
Crop Protection
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Blaschke, T 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65, 216.Google Scholar
Clark, A 2012. Managing Cover Crops Profitably. Project manager and editor, Andy Clark.—3rd ed. Sustainable Agriculture Research & Education (SARE) program, USA.Google Scholar
European Commission 2009. Directive 2009/128/EC of the European Parliament and of the Council establishing a framework for Community action to achieve the sustainable use of pesticides.Google Scholar
FAO 2016. Grassland species. http://www.fao.org/ag/agp/AGPC/doc/gbase/data/Pf000208.HTM. (retrieved 06/11/16).Google Scholar
Hung, C, Xu, Z and Sukkarieh, S 2014. Feature Learning Based Approach for Weed Classification Using High Resolution Aerial Images from a Digital Camera Mounted on a UAV. Remote Sensing 6, 1203712054.Google Scholar
Kataoka, T, Kaneko, T, Okamoto, H and Hata, S 2003. Crop growth estimation system using machine vision. In: The 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings, pp. b1079–b1083 vol.2.Google Scholar
Matese, A, Toscano, P, Di Gennaro, SF, Genesio, L, Vaccari, FP, Primicerio, J, Belli, C, Zaldei, A, Bianconi, R and Gioli, B 2015. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sensing 7, 29712990.Google Scholar
Neto, JC 2004. A combined statistical—soft computing approach for classification and mapping weed species in minimum tillage systems. University of Nebraska, Lincoln, NE, USA.Google Scholar
Otsu, NA 1979. Threshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man Cybern 9, 6266.Google Scholar
Peña, JM, Torres-Sánchez, J, de Castro, AI, Kelly, M and 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.CrossRefGoogle ScholarPubMed
Peña, JM, Torres-Sánchez, J, Serrano-Pérez, A, de Castro, AI and López-Granados, F 2015. Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution. Sensors 15, 56095626.CrossRefGoogle ScholarPubMed
Ripochi, A, Metay, A, Celette, F and Gary, C 2011. Changing the soil surface management in vineyards: immediate and delayed effects on the growth and yield of grapevine. Plant Soil 339, 259271.CrossRefGoogle Scholar
Shi, Y, Thomasson, JA, Murray, SC, Pugh, NA, Rooney, WL et al. 2016. Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research. PLoS ONE 11 (7), e0159781.Google Scholar
Tey, YS and Brindal, M 2012. Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision Agriculture 13, 713730.Google Scholar
Torres-Sánchez, J, López-Granados, F, Serrano, N, Arquero, O and Peña, JM 2015a. High-Throughput 3-D Monitoring of Agricultural-Tree Plantations with Unmanned Aerial Vehicle (UAV) Technology. Plos One PLoS ONE 10 (6), e0130479.Google Scholar
Torres-Sánchez, J, López-Granados, F and Peña, JM 2015b. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture 114, 4352.Google Scholar
Valencia, F, Civit, J, Esteve, J and Recasens, J 2015. Cover-crop management to control Cynodon dactylon in vineyards: balance between efficiency and sustainability. 7th International Weed Science Congress. June 19-25. Prague (Czech Republic).Google Scholar
Wood, EM, Pidgeon, AM, Rade loff, VC and Keuler, NS 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment 121, 516526.Google Scholar