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Multi-sensor imaging retrofit system to test precision agriculture machine-based applications

Published online by Cambridge University Press:  01 June 2017

P. Menesatti ,
F. Pallottino ,
S. Figorilli ,
F. Antonucci ,
R. Tomasone  and
C. Costa
P. Menesatti
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
F. Pallottino*
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
S. Figorilli
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
F. Antonucci
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
R. Tomasone
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
C. Costa
Affiliation:
CREA-ING Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Unità di ricerca per l’ingegneria agraria, Via della Pascolare 16, 00015, Monterotondo scalo (RM), Italy.
*
†E-mail: federico.pallottino@crea.gov.it
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Abstract

An increasing number of farm machines nowadays implement precision agriculture technologies. Most of these operate through proximal sensing using optical sensors (i.e. NIR or Vis-NIR). Imaging techniques in this context have received minor consideration due to the complex analysis of the data but on the other side offer great flexibility. This study reports a preliminary pilot imaging multi-sensor retrofit system to be applied independently on a wide range of agricultural machines and able to test different monitoring or control image-based applications for precision agriculture. The process, based on RGB image, was tested for in-field discrimination of weeds in lettuce and broccoli crops. It works by discriminating and extracting single plants from the soil and weeds. However, to be truly implementable, the experimental code should be optimized in order to shorten the time needed for acquisition and processing.

Keywords

mechanical weedingimage analysismorphometryk-NN
Type
Crop Sensors and Sensing
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 189 - 192
Copyright
© The Animal Consortium 2017 

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References

Aldabaa, AAA, Weindorf, DC, Chakraborty, S, Sharma, A and Li, B 2015. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma 239, 34–46.Google Scholar
Gerhards, R, Sökefeld, M, Peteinatos, G, Nabout, A, Maier, J and Risser, P 2016. Robotic intra-row weed hoeing in maize and sugar beet. Julius-Kühn-Archiv 452, 462.Google Scholar
Menesatti, P, Zanella, A, D’Andrea, S, Costa, C, Paglia, G and Pallottino, F 2009. Supervised multivariate analysis of hyperspectral NIR Images to evaluate the starch index of apples. Food and Bioprocess Technology 2 (3), 308–314.Google Scholar
Pallottino, F, Menesatti, P, Costa, C, Paglia, G, De Salvador, FR and Lolletti, D 2010. Image analysis techniques for automated hazelnut peeling determination. Food and Bioprocess Technology 3 (1), 155–159.CrossRefGoogle Scholar
Pierpaoli, E, Carli, G, Pignatti, E and Canavari, M 2013. Drivers of precision agriculture technologies adoption: a literature review. Procedia Technology 8, 61–69.Google Scholar
Saxena, L and Armstrong, L 2014. A survey of image processing techniques for agriculture. Edith Cowan University Research Online. Last access 10/01/2017. Link: http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1855&context=ecuworkspost2013.Google Scholar
Weis, M and Gerhards, R 2007. Feature extraction for the identification of weed species in digital images for the purpose of site-specific weed control. Precision Agriculture 7, 537–545.Google Scholar
Young, SL, Meyer, GE and Woldt, WE 2014. Future directions for automated weed management in precision agriculture. In Automation: The Future of Weed Control in Cropping Systems. Springer, Netherlands. pp. 249–259.Google Scholar