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

Ayuninghemi, Ratih and Yuana, Dia Bitari Mei and Sjamsijah, Nurul and Perdanasari, Lukie and Hidayatullah, Mohammad and Amal, Iqbal Ikhlasul (2022) Image Segmentation for Oyster Mushroom Grade with Canny Detection for Image Classification. International Journal of Artificial Intelligence Research, 6 (1.2).

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Abstract

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.

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