Automatic dragon fruit counting using adaptive thresholds for image segmentation and shape analysis

In our country today, the counting of dried coconuts at the production facilities is done manually, takes a lot of time and is not accurate. The goal of this study is to build an automatic, fast and accurate coconut counting system. The study was conducted on the peeled dried coconut fruit with a diameter of 15 cm to 20 cm using image processing technology and open-source computer vision library - OpenCV library. The algorithm includes four main steps. First, determine the object and the background using the Otsu segmentation method. Next, estimate the distance between the background and the object to determine the closest area to the center of the object. Then, find the contour, determine the center and area of the object to reduce the noise. The watershed segmentation algorithm is used to separate overlapping and stacking objects. Finally, count the number of objects contained in the image. In the initial experimental results, the counting system has had an accuracy of over 95% with processing time per image about 75 ms and the counting capacity of the system is over 2000 fruits/hour has confirmed the efficiency of the proposed method.

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Automatic dragon fruit counting using adaptive thresholds for image segmentation and shape analysis

Cited by 3 publications

References 10 publications

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Build coconut counting system using image technology

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