Detecting Chili Ripeness Using YOLOv11

Andika Ramadian Pratama, Alfiah Fajriani, Sitti Najmia Rifai

Abstract


This study aims to develop a deep learning-based chili ripeness detection system using the YOLOv11 model. Chili ripeness is classified into three categories: unripe, semi-ripe, and ripe. The dataset consists of 150 original images, which were expanded to 300 images to increase data variation. Model training was conducted using the Roboflow platform, while accuracy testing was performed in Google Colab through an image upload-based processing method. The experimental results show that the model achieved an accuracy of 93.94%, with a precision of 94.21%, recall of 93.94%, and an F1-score of 93.94% on the test dataset. This system is expected to support the automation of chili sorting based on ripeness levels.

Keywords


chili fruit detection; computer vision; deep learning; ripeness classification; YOLOv11

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References


M. Anwar, M. A. Np, and M. Rohman, “Segmentasi Citra Daun dan Cabai menggunakan YOLOv7,” Vol. 3, pp. 1–6, 2024.

Z. He, K. Wang, T. Fang, L. Su, R. Chen, and X. Fei, “Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment,” Proc. 37th Chinese Control Decis. Conf. CCDC 2025, pp. 1281–1286, 2025, DOI: 10.1109/CCDC65474.2025.11090973.

R. Khanam and M. Hussain, “YOLOv11: An Overview of the Key Architectural Enhancements,” Vol. 2024, pp. 1–9, 2024, [Online]. Available: http://arxiv.org/abs/2410.17725

Y. Ma, Z. Peng, and Y. Ruan, “Deep Learning based Pepper Maturity Detection,” Proc. 2024 8th Int. Conf. Electron. Inf. Technol. Comput. Eng. EITCE 2024, No. January, pp. 300–304, 2025, DOI: 10.1145/3711129.3711182.

J. Song, D. Kim, E. Jeong, and J. Park, “Determination of Optimal Dataset Characteristics for Improving YOLO Performance in Agricultural Object Detection,” Agric., Vol. 15, No. 7, 2025, DOI: 10.3390/agriculture15070731.

I. Esaki, S. Noma, T. Ban, R. Sultana, and I. Shimizu, “Maturity Classification of Blueberry Fruit using YOLO and Vision Transformer for Agricultural Assistance †,” Horticulturae, Vol. 11, No. 10, pp. 1–14, 2025, DOI: 10.3390/horticulturae11101272.

L. Tandoballa and E. Hartati, “Implementation of the You Look Only Once ( YOLOv11 ) Algorithm to Detect the Ripeness of Golden Melons,” Vol. 5, No. 2, pp. 191–204, 2025.

J. Liu, J. Guo, and S. Zhang, “YOLOv11-HRS: An Improved Model for Strawberry Ripeness Detection,” Agronomy, Vol. 15, No. 5, 2025, DOI: 10.3390/agronomy15051026.

R. Zhang, “YOLOv11-BSD: Blueberry Maturity Detection under Simulated Nighttime Conditions Evaluated with Causal Analysis,” Smart Agric. Technol., Vol. 12, No. July, 2025, DOI: 10.1016/j.atech.2025.101314.

A. S. Alzaharani and A. Iqbal, “A Multi-Stage Pipeline for Date Fruit Processing: Integrating YOLOv11 Detection, Classification, and Automated Counting,” Comput. Mater. Contin., Vol. 86, No. 1, pp. 1–27, 2026, DOI: 10.32604/cmc.2025.070410.

Y. Liu, Q. Yu, S. Geng, S. Guo, and L. Liu, “SSViT-YOLOv11: Fusing Lightweight YOLO & ViT for Coffee Fruit Maturity Detection,” Front. Plant SCI., Vol. 16, No. December, pp. 1–19, 2025, DOI: 10.3389/fpls.2025.1691643.

B. Sunarko, “Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara,” Edu Komputika J., Vol. 10, No. 1, pp. 55–63, 2023, DOI: 10.15294/edukomputika.v10i1.72080.

S. Cepeda, “Real-Time Brain Tumor Detection in Intraoperative Ultrasound: From Model Training to Deployment in the Operating Room,” Comput. Biol. Med., Vol. 193, pp. 1–27, 2025, DOI: 10.1016/j.compbiomed.2025.110481.

F. Schneider, J. Swiatek, and M. Jelali, “Detection of Growth Stages of Chilli Plants in a Hydroponic Grower using Machine Vision and YOLOv8 Deep Learning Algorithms,” Sustain., Vol. 16, No. 15, 2024, DOI: 10.3390/su16156420.

L. Nury, I. Afida, F. A. Bachtiar, and I. Cholissodin, “Klasifikasi Aktivitas Manusia menggunakan Metode Long Short-Term Memory classification of Human Activity using Long Short-Term Memory,” J. Teknol. Inf. dan Ilmu Komput., Vol. 11, No. 11, pp. 357–368, 2024, DOI: 10.25126/jtiik.2024117060.

D. R. B. Lubis and A. Armansyah, “Klasifikasi Mahasiswa Berpotensi Dropout menggunakan Metode Regresi Logistik,” Techno.Com, Vol. 24, No. 3, pp. 768–778, DOI: 10.62411/tc.v24i3.13526.

K. A. Sahib, B. K. Oleiwi, and A. R. Nasser, “Medicinal Plants Recognition using Deep Transfer Learning Models,” Int. J. Des. Nat. Ecodynamics, Vol. 19, No. 5, pp. 1501–1510, DOI: 10.18280/ijdne.190504.

T. Selvakumar, M. N. A. H. Sha‘abani, M. S. . Aras, and M. B. Bahar, “Development of a YOLOv11-based Deep Learning System for Insect Pest Detection and Classification in Oil Palm Plantation,” Mod. Appl. SCI., Vol. 19, No. 2, p. 32, 2025, DOI: 10.5539/mas.v19n2p32.

Wulandari, Sasmita, M. R. Mulia, A. B. Kaswar, D. D. Andayani, and A. S. Agung, “Klasifikasi Kandungan Nutrisi Buah Pisang berdasarkan Fitur Tekstur dan Warna LAB menggunakan Jaringan Syaraf Tiruan berbasis Pengloahan Citra Digital,” J. Teknol. Inf. dan Ilmu Komput., Vol. 11, No. 3, pp. 507–518, 2024, DOI: 10.25126/jtiik.938332.




DOI: https://doi.org/10.32520/stmsi.v15i2.6122

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