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Article | Green Intelligent Systems and Applications | Volume 5 - Issue 2 - 2025 | 191−204 | https://doi.org/10.53623/gisa.v5i2.934
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Implementation of the You Look Only Once (YOLOv11) Algorithm to Detect the Ripeness of Golden Melons

Author(s): Lucky Tandoballa , Ery Hartati
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Faculty of Computer Science and Engineering, Universitas Multi Data Palembang, South Sumatra, Indonesia

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Green Intelligent Systems and Applications

Volume 5 - Issue 2 - 2025

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Melon is a horticultural commodity with high economic value, and characteristics such as sweetness, aroma, texture, and phytonutrient content significantly influenced consumer preference. Conventional methods for determining melon ripeness were time-consuming, required considerable expertise, and were often prone to subjective errors, ultimately slowing the production and distribution process. This study aimed to detect the ripeness level of golden melon fruit non-destructively using the YOLOv11 algorithm, focusing on external physical characteristics as the basis for classification. The objectives included applying transfer learning to categorize golden melon into ripe and unripe classes and evaluating model performance using precision, recall, mAP50, mAP50-95, and F1-score. The research methodology consisted of a literature review, dataset collection from previous studies, system design, implementation, and performance testing. The dataset was divided into 70% training, 20% validation, and 10% testing data, and the Adam optimizer was used during the training phase. Based on four experimental scenarios, scenario 3 produced the best and most consistent results, achieving a precision of 90.58%, a recall of 90.79%, an mAP50 of 97.31%, an mAP50-95 of 88.84%, and an F1-score of 92.97%. These findings demonstrated that scenario 3 offered optimal performance for detecting golden melon ripeness. Thus, the model was highly reliable overall.

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SUBMITTED: 30 November 2025
ACCEPTED: 23 December 2025
PUBLISHED: 25 December 2025
SUBMITTED to ACCEPTED: 23 days
DOI: https://doi.org/10.53623/gisa.v5i2.934

Cite this article
Tandoballa, L., & Hartati, E. (2025). Implementation of the You Look Only Once (YOLOv11) Algorithm to Detect the Ripeness of Golden Melons. Green Intelligent Systems and Applications, 5(2), 191−204. https://doi.org/10.53623/gisa.v5i2.934
Keywords
adam optimizer melon ripness detection object detection YOLOv11
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