Nurnajwa Erwina Md Rosli, Samsul Setumin, Anan Nugroho, Adi Izhar Che Ani, Mohd Ikmal Fitri Maruzuki, Mohamed Syazwan Osman
In this study, image segmentation on Ficus Carica (fig) was developed. Fig fruit image segmentation separates fruit objects by removing the background in the image, including shadow images, and extracting the fruit shape. The developed methods for fig image segmentation were evaluated to identify how well the methods work and were compared to find the best method for fig image segmentation. As a reference, ground truth was made using software called Procreate for comparative purposes. There were three methods used in this paper that include Threshold, K-means clustering, and Sharp U-Net. The platform used for this development is Google Colab. Based on the results obtained, the Sharp U-Net demonstrates the highest value of accuracy as compared to the Threshold and K-means Clustering techniques. Therefore, the most effective and efficient method to use on fig fruit image segmentation is the Sharp U-Net method. © 2022 IEEE.
Universiti Teknologi MARA, Cawangan Pulau Pinang, Centre for Electrical Engineering Studies, Pulau Pinang, 13500, Malaysia; Universitas Negeri Semarang Kampus UNNES, Jurusan Teknik Elektro, Fakultas Teknik, Jawa Tengah, 50229, Indonesia; Universiti Teknologi MARA, Cawangan Pulau Pinang, EMZI-UiTM Nanoparticles Colloids Interface Indust. Res. Lab, Sch. of Chem. Eng., Coll. of Eng, Pulau Pinang, 13500, Malaysia
