Automated, low-cost yield mapping of wild blueberry fruit

K.C. Swain; Q.U. Zaman; A.W. Schumann; D.C. Percival; D. Nams; T.J. Esau

Automated, low-cost yield mapping of wild blueberry fruit

in Precision agriculture '09

Autor:innen:

K.C. Swain

K.C. Swain Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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Q.U. Zaman

Q.U. Zaman Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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A.W. Schumann

A.W. Schumann Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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D.C. Percival

D.C. Percival Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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D. Nams

D. Nams Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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T.J. Esau

T.J. Esau Engineering Department, Nova Scotia Agricultural College, Truro, Nova Scotia, B2N 5E3, Canada; swainkc@yahoo.com

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Art:: Kapitel

Seiten:: 381–388

DOI:: https://doi.org/10.1163/9789086866649_046

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Abstract

The presence of weeds, bare spots, variation in soil/crop characteristics and fruit yield within the wild blueberry field requires more accurate applications to maximize profit and reduce environmental risks. The objective of this research was to develop an Automated Yield Monitoring and Mapping System to produce real-time fruit yield map for site-specific application of agrochemicals. This AYMMS was mounted on a specially designed Farm Motorized Vehicle (FMV). The AYMMS consists of a 10-mega pixel, 24-bit digital color camera (Canon Canada Inc., Mississauga, ON, Canada), Trimble Ag GPS 332 (Trimble Navigation Limited, Sunnyvale, CA) for geo-referencing and a laptop computer. Custom software was developed in ‘Delphi’ and ‘C’ programs for image processing to estimate %BPI (blue pixel index) representing ripe fruit. Two wild blueberry fields were selected in central Nova Scotia to evaluate the yield monitoring system. Percentage blue pixels correlated highly significantly with actual fruit yield in field 1 (R²=0.90; P<0.001; n=19) and field 2 (R²=0.97; P<0.001; n=19). The correlation between actual and predicted fruit yield in field 2 (validation) was also highly significant (R²=0.97; P<0.001; n=19). Real time yield mapping was carried out with AYMMS by acquiring images on the moving FMV at a spacing of 1.25 m and a ground speed of 0.5 m/sec. The estimated yield per image field of view along with geo-referenced coordinates was imported into Arc View 3.2 GIS (ESRI, Redlands, Calif, USA) to map fruit yield for the two selected fields. The maps showed substantial variability in fruit yield in both fields. The yield maps along with soil maps could be used for variable rate application of site specific crop inputs.

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Precision agriculture '09

ISBN:: 9789086866649

Verleger:: Wageningen Academic

Print-Publikationsdatum:: 01 Jan 2009

Fachgebiete
- Biowissenschaften
  - Allgemein
  - Landwirtschaft und Umwelt

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Automated, low-cost yield mapping of wild blueberry fruit

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Precision agriculture '09

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