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Machine vision for spot-application of agrochemical in wild blueberry fields

Published online by Cambridge University Press:  01 June 2017

T. Esau ,
Q. Zaman ,
D. Groulx ,
Y. Chang ,
A. Schumann  and
P. Havard
T. Esau*
Affiliation:
Faculty of Agriculture, Dalhousie University, Nova Scotia, B2N 5E3, Canada
Q. Zaman
Affiliation:
Faculty of Agriculture, Dalhousie University, Nova Scotia, B2N 5E3, Canada
D. Groulx
Affiliation:
Faculty of Engineering, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada
Y. Chang
Affiliation:
Faculty of Agriculture, Dalhousie University, Nova Scotia, B2N 5E3, Canada
A. Schumann
Affiliation:
Citrus Research Center, University of Florida, Lake Alfred, Florida, 33850-2299, United States
P. Havard
Affiliation:
Faculty of Agriculture, Dalhousie University, Nova Scotia, B2N 5E3, Canada
*
† E-mail: [email protected]
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Abstract

The goal of the project was to supply growers with knowledge on how incorporation of machine vision technology can affect the wild blueberry crop, disease pressures, and the overall savings of select agrochemical inputs. A machine vision system was developed and mounted on a rear sprayer boom in front of the sprayer nozzles capable of targeting the agrochemical application on an as-needed basis. Results showed that plants that received the proper fungicide application were less prone to premature leaf drop resulting in larger stem diameters and higher bud counts and harvestable fruit yield. Fungicide application savings using the smart sprayer for spot-application was 12% as compared to a uniform application.

Keywords

smart sprayerdigital color cameraweed detectionreal-timeimage processing
Type
Crop Protection
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. 272 - 276
Copyright
© The Animal Consortium 2017 

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References

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