Paper
12 May 1993 Considerations for implementing machine vision for detecting watercore in apples
Bruce L. Upchurch, James A. Throop
Author Affiliations +
Proceedings Volume 1836, Optics in Agriculture and Forestry; (1993) https://doi.org/10.1117/12.144039
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
Abstract
Watercore in apples is a physiological disorder that affects the internal quality of the fruit. Growers can experience serious economic losses due to internal breakdown of the apple if watercored apples are placed unknowingly into long term storage. Economic losses can also occur if watercore is detected and the entire `lot' is downgraded; however, a gain can be obtained if watercored fruit is segregated and marketed as a premium apple soon after harvest. Watercore is characterized by the accumulation of fluid around the vascular bundles replacing air spaces between cells. This fluid reduces the light scattering properties of the apple. Using machine vision to measure the amount of light transmitted through the apple, watercored apples were segregated according to the severity of damage. However, the success of the method was dependent upon two factors. First, the sensitivity of the camera dictated the classes of watercore that could be detected. A highly sensitive camera could separate the less severe classes at the expense of not distinguishing between the more severe classes. A second factor which is common to most quality attributes in perishable commodities is the elapsed time after harvest at which the measurement was made. At the end of the study, light transmission levels decreased to undetectable levels with the initial camera settings for all watercore classes.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bruce L. Upchurch and James A. Throop "Considerations for implementing machine vision for detecting watercore in apples", Proc. SPIE 1836, Optics in Agriculture and Forestry, (12 May 1993); https://doi.org/10.1117/12.144039
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Cited by 2 scholarly publications.
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KEYWORDS
Cameras

Imaging systems

Machine vision

Agriculture

Forestry

Light scattering

Tissue optics

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