Real-Time Detection of Coffee Cherry Ripeness Using YOLOv11
DOI:
https://doi.org/10.30871/jaic.v9i4.9735Keywords:
Python, Automated Detection System, Coffee Fruit, YOLOv11, Machine LearningAbstract
This study aims to develop a real-time coffee fruit ripeness detection system using the YOLOv11 algorithm to assist farmers in determining the optimal harvest time. The dataset comprises 302 images categorized into three ripeness levels: ripe, semi-ripe, and unripe. Model training was conducted on Google Colab with data augmentation to enhance dataset variability and prevent overfitting. After 20 epochs, the model demonstrated strong performance in the ripe category (mAP50: 0.774, Precision: 0.645, Recall: 0.812) and satisfactory results for semi-ripe fruits (mAP50: 0.695, Precision: 0.624, Recall: 0.679). However, detection performance for unripe fruits was lower (mAP50: 0.4). The system achieved an inference time of 183.4 ms per image, with fast preprocessing and postprocessing (0.5 ms each), indicating its suitability for real-time applications. While the model performs well overall, further improvement is needed in detecting unripe coffee fruits for enhanced system effectiveness.
Downloads
References
[1] M. S. Hawibowo and I. Muhimmmah, “Aplikasi Pendeteksi Tingkat Kematangan Pepaya menggunakan Metode Convolutional Neural Network (CNN) Berbasis Android,” J. Edukasi dan Penelit. Inform., vol. 10, no. 1, p. 162, 2024, doi: 10.26418/jp.v10i1.77819.
[2] Nurdin, Bustami, and Maryana, “Robust optimization approach for agricultural commodity supply chain planningg,” J. Theor. Appl. Inf. Technol., vol. 99, no. 2, pp. 304–315, 2021.
[3] J. Rusman and N. Pasae, “Prototype Sistem Penyortir Buah Kopi Arabika Berdasarkan Tingkat Kematangan Menggunakan Metode Support Vector Machine,” Teknika, vol. 12, no. 1, pp. 65–72, 2023, doi: 10.34148/teknika.v12i1.602.
[4] S. R. Raysyah, Veri Arinal, and Dadang Iskandar Mulyana, “Klasifikasi Tingkat Kematangan Buah Kopi Berdasarkan Deteksi Warna Menggunakan Metode Knn Dan Pca,” JSiI (Jurnal Sist. Informasi), vol. 8, no. 2, pp. 88–95, 2021, doi: 10.30656/jsii.v8i2.3638.
[5] C. R. Gunawan, N. Nurdin, and F. Fajriana, “Deteksi Ikan Segar Secara Realtime dengan YOLOv4 menggunakan Metode Convolutional Neural Network,” J. Komtika (Komputasi dan Inform., vol. 7, no. 1, pp. 1–11, 2023, doi: 10.31603/komtika.v7i1.8986.
[6] J. Ulfah and N. Nurdin, “Implementasi Metode Deteksi Tepi Canny Untuk Menghitung Jumlah Uang Koin Dalam Gambar Menggunakan Opencv,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3, pp. 420–426, 2023, doi: 10.23960/jitet.v11i3.3147.
[7] K. Azman, M. Arhami, and A. Azhar, “Metode You Only Look Once (YOLO) dalam Deteksi Physical Distancing dan Wajah Bermasker,” Pros. Semin. Nas. Politek. Negeri Lhokseumawe, vol. 6, no. 1, pp. 107–113, 2022.
[8] A. Rifqi Akyas hifdzi Rahman, Asril Adi Sunarto, “Penerapan (You Only Look Once) V8 Untuk Deteksi Tingkat Kematangan Buah Manggis,” vol. 8, no. 5, pp. 10566–10571, 2024.
[9] R. Sapkota, Z. Meng, M. Churuvija, X. Du, Z. Ma, and M. Karkee, “Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments,” 2024.
[10] P. Hanifah, H. I. Antoni, S. R. Ramadhani, and Yuliska, “Pengembangan Aplikasi Mobile untuk Deteksi Cacat Biji Kopi Robusta Berdasarkan Standar Nasional Indonesia,” pp. 17–26, 2018.
[11] M. F. Golfantara, “Penggunaan Algoritma YOLO V8 Untuk Identifikasi Rempah-Rempah,” vol. 12, no. 3, pp. 3867–3873, 2024.
[12] L. Rahma, H. Syaputra, A. H. Mirza, and S. D. Purnamasari, “Objek Deteksi Makanan Khas Palembang Menggunakan Algoritma YOLO (You Only Look Once),” J. Nas. Ilmu Komput., vol. 2, no. 3, pp. 213–232, 2021, doi: 10.47747/jurnalnik.v2i3.534.
[13] N. Khairunisa, . C., and A. Jamaludin, “Analisis Perbandingan Algoritma Cnn Dan Yolo Dalam Mengidentifikasi Kerusakan Jalan,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4434.
[14] R. Hesananda, D. Natasya, and N. Wiliani, “Cloth Bag Object Detection Using the Yolo Algorithm (You Only See Once) V5,” J. Pilar Nusa Mandiri, vol. 18, no. 2, pp. 217–222, 2023, doi: 10.33480/pilar.v18i2.3019.
[15] C. R. Gunawan, N. Nurdin, and F. Fajriana, “Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once),” Int. J. Eng. Sci. Inf. Technol., vol. 2, no. 3, pp. 96–99, 2022, doi: 10.52088/ijesty.v2i3.309.
[16] Y. Yanto, F. Aziz, and I. Irmawati, “Yolo-V8 Peningkatan Algoritma Untuk Deteksi Pemakaian Masker Wajah,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 3, pp. 1437–1444, 2023, doi: 10.36040/jati.v7i3.7047.
[17] Z. Hong et al., “Multi-Scale Ship Detection from SAR and Optical Imagery Via A More Accurate YOLOv3,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 6083–6101, 2021, doi: 10.1109/JSTARS.2021.3087555.
[18] S. Du, P. Zhang, B. Zhang, and H. Xu, “Weak and Occluded Vehicle Detection in Complex Infrared Environment Based on Improved YOLOv4,” IEEE Access, vol. 9, pp. 25671–25680, 2021, doi: 10.1109/ACCESS.2021.3057723.
[19] A. Putra Pranjaya, F. Rizki, R. Kurniawan, and N. K. Daulay, “Penyakit Pada Daun Tanaman Padi Berbasis YoloV5 (You Only Look Once),” Media Online), vol. 4, no. 6, pp. 3127–3136, 2024, doi: 10.30865/klik.v4i6.1916.
[20] Y. Zhou et al., “Adaptive Active Positioning of Camellia oleifera Fruit Picking Points: Classical Image Processing and YOLOv7 Fusion Algorithm,” Appl. Sci., vol. 12, no. 24, 2022, doi: 10.3390/app122412959.
[21] H. Wang, X. Xu, Y. Liu, D. Lu, B. Liang, and Y. Tang, “Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms,” Appl. Sci., vol. 13, no. 12, 2023, doi: 10.3390/app13126898.
[22] H. T. Vo, K. C. Mui, N. N. Thien, and P. P. Tien, “Automating Tomato Ripeness Classification and Counting with YOLOv9,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 4, pp. 1120–1128, 2024, doi: 10.14569/IJACSA.2024.01504113.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Anis Ilyana, Nurdin Nurdin, Maryana Maryana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
