Weight and Diameter Estimation Using Image Processing and Machine Learning Techniques on Apple Images
Abstract
Classification of table fruits according to size
is traditionally hand made. But human factors are the cause of faulty
classifications. Automatically performing this process with the machines is
important in terms of speeding up the process, reducing costs, and minimizing
errors. In this study, weight and diameter estimations were made on
"Starking" type apples using image processing techniques. Firstly 50
photographs were taken with NIR camera and 830nm long pass filter. Afterwards,
edge detection algorithms and morphological operations were performed on the
images to obtain the boundaries of the images. Diameter and area information
obtained from the binary image were used as attributes. These attributes were
given as input to Linear Regression method and estimated. As a result, 93% of
the diameters of the apples and 96.5% of the weights could be estimated.
Keywords
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