Abstract
Agriculture-related works include harvesting, sorting, and packaging etc. Those works require lots of time and a huge number of expert resources for manual execution. Automation may be the solution to this problem. There are lots of challenges for the automation of those works with the help of image processing. One of the major challenges is the identification of fruit and vegetable class accurately from various viewing positions. In this paper, a viewpoint independent solution is proposed for fruit and vegetable classification. Firstly, input RGB color image is converted to a grayscale image. Multiple threshold values are calculated from the grayscale image using multi-level thresholding technique. Then a set of the binary images is generated using those threshold values. In the next step, the border image is extracted from each of the binary images. Finally, the fractal dimension is computed from the border image and used to classify the fruit and vegetable. The proposed method was tested on a dataset of 1080 images, which contains 15 classes of fruits and vegetables. Complete 360° viewing positions are considered for experimentation. The range of overall system accuracy is 97.78% to 100% using k-NN classifier.
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Jana, S., Parekh, R., Sarkar, B. (2019). An Approach Towards Classification of Fruits and Vegetables Using Fractal Analysis. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2018. Communications in Computer and Information Science, vol 1031. Springer, Singapore. https://doi.org/10.1007/978-981-13-8581-0_14
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DOI: https://doi.org/10.1007/978-981-13-8581-0_14
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