Classification Types of Apples In Real Time Using SSD Mobilenet

Apples are one type of fruit commodity that is very easy and often found in Indonesia. In classifying the types of apples, it is determined by several parameters, including weight, size, weight, characteristics, color, and many others. The grouping of apples in terms of shape and skin color is an important factor in the identification process. At the stage of identifying the type of apple manually by the human eye, the perception tends to be subjective due to color composition factors such as royal gala apples, fuji apples, and Washington apples. Therefore, we need a tool with a system that can select apples based on the type. This real-time apple type classifier will use the SSD-MobileNet method which is used for feature extraction and the SSD model will be used for object detection in the image. This study resulted in an accuracy of 92.6% for Royal Gala Apples 94.7%, Fuji Apples 92%, and Washington Apples 91.2%.

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Published
2024-10-17
DOI
https://doi.org/10.1109/ceria64726.2024.10914729
Primary Topic
Smart Agriculture and AI
Type
article
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article

Classification Types of Apples In Real Time Using SSD Mobilenet

Ainul Hizriadi, Fahrurrozi Lubis, Arya Pratama Tarigan, Deddy F Sihombing
Smart Agriculture and AI
article

Classification Types of Apples In Real Time Using SSD Mobilenet

Ainul Hizriadi, Fahrurrozi Lubis, Arya Pratama Tarigan, Deddy F Sihombing
article en

Abstract

Apples are one type of fruit commodity that is very easy and often found in Indonesia. In classifying the types of apples, it is determined by several parameters, including weight, size, weight, characteristics, color, and many others. The grouping of apples in terms of shape and skin color is an important factor in the identification process. At the stage of identifying the type of apple manually by the human eye, the perception tends to be subjective due to color composition factors such as royal gala apples, fuji apples, and Washington apples. Therefore, we need a tool with a system that can select apples based on the type. This real-time apple type classifier will use the SSD-MobileNet method which is used for feature extraction and the SSD model will be used for object detection in the image. This study resulted in an accuracy of 92.6% for Royal Gala Apples 94.7%, Fuji Apples 92%, and Washington Apples 91.2%.

Universitas Sumatera Utara (ID)
Openalex Percentile: Top 84%
Smart Agriculture and AI
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Classification Types of Apples In Real Time Using SSD Mobilenet — Ainul Hizriadi, Fahrurrozi Lubis, et al. · (2024) | TGRS Research Map | TGRS