Using computer vision for detecting watercore in apples

James A. Throop; Gerald E. Rehkugler; Bruce L. Upchurch

doi:10.1117/12.25082

1 February 1991 Using computer vision for detecting watercore in apples

James A. Throop, Gerald E. Rehkugler, Bruce L. Upchurch

Proceedings Volume 1379, Optics in Agriculture; (1991) https://doi.org/10.1117/12.25082
Event: Advances in Intelligent Robotics Systems, 1990, Boston, MA, United States

Abstract

Methods for quantifying watercore damage in Red Delicious apples are described that measures visible and/or near-infrared (NIR) radiation transmitted through the core of individual apples. A linear regression of the data for NIR light alone and on the data for the combination of NIR and visible light produced R squared values from . 90 to . 96 indicating little difference between the different data sets. The combination of visible and NIR works equally as well as NIR alone. Images captured by a low-light level and an image-intensifier equipped black and white camera are compared to study the effect of sensor intensity sensitivity in the classification process. The intensified image results show the mean probability of an error as 1 3. 2 percent for under classifying the five different classes of watercore damage compared to 24. 9 and 27. 5 (NIR NIR and visible) for the low - light level camera. The mean probability of an error of over classifying from the intensified images is 17. 1 percent compared to 24. 0 and 27. 2 (NIR NIR and visible) for the low -light level camera. Increases in intensity sensitivity can improve the classification of watercore damage. Two computational methods to numerically quantify watercore damage are examined and compared. The first method finds the mean grey level within a 130 pixel (rows) by 100 pixel (columns) window centered about the stem end of

Citation Download Citation

James A. Throop, Gerald E. Rehkugler, and Bruce L. Upchurch "Using computer vision for detecting watercore in apples", Proc. SPIE 1379, Optics in Agriculture, (1 February 1991); https://doi.org/10.1117/12.25082

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