Developed a Tomato-Condition Classification System using Image Processing and YOLO Technology

Classifying tomatoes after harvesting still heavily relies on manual labor, leading to inefficiencies and inconsistent quality. This issue affects the cost and reduces the ability to export to important international markets. This study introduces a modern solution by applying the YOLO algorithm to...

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Bibliographic Details
Published in2024 9th International Conference on Integrated Circuits, Design, and Verification (ICDV) pp. 108 - 113
Main Authors Ba, Quang - Huy Do, Pham, Van-Nam, Nguyen, Van-Thanh, Nguyen, Quang-Minh, Vo, Ba-Thong, Ha, Manh-Hung, Kim, Dinh-Thai
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.06.2024
Subjects
Automatic classification system
Computer vision
Costs
Image processing
Integrated circuit synthesis
Machine vision
Manuals
Neural networks
Shape
Tomato
YOLO
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Summary:Classifying tomatoes after harvesting still heavily relies on manual labor, leading to inefficiencies and inconsistent quality. This issue affects the cost and reduces the ability to export to important international markets. This study introduces a modern solution by applying the YOLO algorithm to automate the tomato classification process. The automatic classification system is proposed, which not only reduces cost labor but also improves performance reaching approximately 98 \% on TOMATO dataset. This system utilizes image processing technology to assess size, color (including ripe, unripe, and rotten tomatoes), and shape (recognizing abnormalities, surface scratches,...). Additionally, the research emphasizes the importance of applying computer vision technology and utilizing self-created datasets to ensure high performance in the real-world environment of tomatoes, thus opening up prospects for technology application in agriculture.
DOI:10.1109/ICDV61346.2024.10616990