Application of Convolution Neural Network Algorithm for Rice Type Detection Using Yolo v3
Penerapan Algoritma Convolution Neural Network untuk Deteksi Jenis Padi Menggunakan Yolo v3
DOI:
https://doi.org/10.35706/sys.v3i2.5874Abstract
Rice is a staple food that contains a lot of energy for human life. There are several types of rice that are often sold in rice shops in general, namely IR42 rice, Pera rice, glutinous rice and Pandan fragrant rice. For now, there are still many people who do not recognize the 4 types of rice, especially millennials, for this reason, research is carried out on the introduction of rice types. The purpose of this study is to make it easier for buyers to identify the type of rice that is in the rice shop so as to minimize fraud by rice traders. The method used in this study is the YOLO (You Only Look Once) v3 method for detecting rice types. The implementation of the image detection process using YOLO (You Only Look Once) v3 has been tested for 12 samples. Based on the results of testing 12 detection experiments on digital image objects, it was obtained 100% where in the picture there were 4 types of rice, 4 grains of rice and 3 types of rice shapes.
Downloads
References
Abi Rachman Wasril, D. (2020). Pembuatan Pendekteksi Obyek Dengan Metode You Only Look Once (YOLO) untuk Automated Teller Machine (ATM). Majalah Ilmiah UNIKOM.
Alexey AB, “AlexeyAB/darknet: Windows and Linux version of Darknet Yolo v3 v2 Neural Networks for object detection (Tensor Cores are used),” GitHub. 2019.
B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, and K. Ouni, “Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3,” 2019 1st Int. Conf. Unmanned Veh. Syst. UVS 2019, pp. 1–6, 2019
Dedy Agung Prabowo, D. (2018). Deteksi dan Perhitungan Objek Berdasarkan Warna menggunakan COlor Objek Tracking. Jurnal Pseudocode, Volume V nomor 2.
Hr.Wibi Bagas, D. (2020). Deteksi Buah untuk Klasifikasi Berdasarkan Jenis Dengan Algoritma Berbasis YOLOv3. Jurnal Resti (Rekayasa Sistem dan Teknologi Informasi), Vol.4 No.3.
J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” 2018.
Muhammad Alfin Jimly Asshiddiqie, D. (2020). Deteksi Tanaman Tebu Pada Lahan Pertanian Menggunakan Metode Convolutional Neural Network. Jurnal Informatika dan Sistem Informasi Vol.1 No.1.
M. Ju, H. Luo, Z. Wang, B. Hui, and Z. Chang, “The application of improved YOLO V3 in multi- scale target detection,” Appl. Sci., vol. 9, no. 18, 2019.
Pandu Satrio, “Pengaruh Drone Terhadap Citra Yang Dihasilkan Pada Pemantauan Tanaman Padi” Sunita Nayak, “Training YOLOv3 : Deep Learning based Custom Object Detector | Learn OpenCV,”Learn OpenCV,2019.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 SYSTEMATICS
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-ShareAlike 4.0 International License. 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).
