Image Segmentation for Oyster Mushroom Grade with Canny Detection for Image Classification

(1) Ratih Ayuninghemi Mail (politeknik negeri jember, Indonesia)
(2) * Dia Bitari Mei Yuana Mail (politeknik negeri jember, Indonesia)
(3) Nurul Sjamsijah Mail (politeknik negeri jember, Indonesia)
(4) Lukie Perdanasari Mail (politeknik negeri jember, Indonesia)
(5) Mohammad Hidayatullah Mail (politeknik negeri jember, Indonesia)
(6) Iqbal Ikhlasul Amal Mail (Politeknik Negeri Jember)
*corresponding author

Abstract


Product quality must remain good to consumers and expand market segmentation to increase income and improve farmers' welfare, post-harvest handling needs to be done. One of the post-harvest handlings of fresh oyster mushroom products is grading. The grading process is carried out based on the quality of the oyster mushroom harvest which is classified into three, namely Grade A, Grade B, and Grade C. Computer technology with digital image processing segmentation and image classification using canny edge detection can be the first step in the process of grading fresh oyster mushrooms. so that the image can be processed for canny detection, it is necessary to do image segmentation. From the results of thresholding on the oyster mushroom image, the threshold value of T is obtained, namely with T1 below 50 and T2 above 150. The T threshold value is a classification for the canny detection process. Of the six oyster mushroom datasets, five datasets of oyster mushrooms were obtained accurately, while one mushroom had broken lines and noise.

Keywords


Oyster Mushroom; Grayscale; Image Acquisition; Canny; Edge Detection; Active Contour;

   

DOI

https://doi.org/10.29099/ijair.v6i1.2.468
      

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