Design of Livestream Video System and Classification of Rice Disease

Maria Agustin - Jakarta State Polytechnic, 16425, Indonesia
Indra Hermawan - Jakarta State Polytechnic, 16425, Indonesia
Defiana Arnaldy - Jakarta State Polytechnic, 16425, Indonesia
Asep Taufik Muharram - Jakarta State Polytechnic, 16425, Indonesia
Bambang Warsuta - Jakarta State Polytechnic, 16425, Indonesia

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One of the agricultural products which is an important aspect of the life of Indonesian people is rice. Rice disease has a devastating effect on rice production, while detecting rice diseases in real-time is still difficult. Therefore, this study designed a Livestream video system that is equipped with a rice disease Classification system. The Livestream system utilizes 4G network communication and is assisted by the WebSocket protocol to communicate in real-time and for the rice disease Classification system using YOLO algorithm. In addition, Livestream uses the raspberry pi camera V2 to take video stream data. In analyzing the performance of the Livestream system, four tests were carried out, namely: functionality test, connectivity test, classification performance test, and implementation performance test. The test was carried out using the wireshark and conky tools, while the classification training used 5447 images from the Huy Minh do dataset that he provided on the Kaggle website. The results show that all programs run well and get a good QoS value according to the index of the parameter results, it is also found that sending non-base64 can reduce the size of the data to approximately 200,000 bytes/s and the performance of the classification system is good because it has an average accuracy of 80% even though it is quite burdening the raspberry pi. This system can still be optimized and developed further to support research in the field of data transmission and the performance of machine learning in a microcontroller.


Livestream; unmanned aerial vehicles; WebSocket protocol; agricultural; rice disease; YOLO.

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“Analisis Biofisik Tanaman Padi dengan Citra Drone (UAV) Menggunakan Software Agisoft Photoscan,†pp. 2–3, 2017.

V. Barannik, S. Podlesny, D. Tarasenko, D. Barannik, and O. Kulitsa, "The video stream encoding method in infocommunication systems," in 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 2018, pp. 538–541. doi: 10.1109/TCSET.2018.8336259.

R. R. Rachmawati, “Smart Farming 4.0 Untuk Mewujudkan Pertanian Indonesia Maju, Mandiri, Dan Modern,†Forum Penelit. Agro Ekon., vol. 38, no. 2, p. 137, 2021, doi: 10.21082/fae.v38n2.2020.137-154.

A. Wongkitrungrueng and N. Assarut, "The role of live streaming in building consumer trust and engagement with social commerce sellers," J. Bus. Res., vol. 117, no. November 2017, pp. 543–556, 2020, doi: 10.1016/j.jbusres.2018.08.032.

G. M. B. Oliveira et al., "Comparison between MQTT and WebSocket Protocols for IoT Applications Using ESP8266," 2018 Work. Metrol. Ind. 4.0 IoT, MetroInd 4.0 IoT 2018 - Proc., pp. 236–241, 2018, doi: 10.1109/METROI4.2018.8428348.

Z. A. Achmad, “Penerapan Websocket Untuk Transmisi Data Pada Iot (Internet of Things) Guna Mendukung Era Industri 4.0,†2019.

M. Lutfi, P. H. Trisnawan, and R. Primananda, “Implementasi Routing Statis menggunakan Media Komunikasi LoRa dan Websocket untuk Pengiriman Data dari Sensor ke Cloud pada IoT,†vol. 5, no. 12, pp. 5339–5348, 2021.

B. Soewito, Christian, F. E. Gunawan, Diana, and I. Gede Putra Kusuma, "Websocket to support real time smart home applications," Procedia Comput. Sci., vol. 157, pp. 560–566, 2019, doi: 10.1016/j.procs.2019.09.014.

F. P. Uditama, R. Primananda, and M. Data, “Perancangan Aplikasi Pemantauan Pendaki Gunung Menggunakan Wireless Network Dengan Protokol MQTT,†J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya e-ISSN 2548-964X, vol. 2 No 5, no. 5, pp. 2102–2108, 2018.

Juslenius, S. WebSocket vs WebRTC in the stream overlays of the Streamr Network. University of Helsinki. 2021.

K. E. Ogundeyi and C. Yinka-Banjo, "WebSocket in real time application," Niger. J. Technol., vol. 38, no. 4, p. 1010, 2019, doi: 10.4314/njt.v38i4.26.

A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," 2020.

V. Gupta Pola, A. Bhavya Vaishnavi, and S. Suraj Karra, "Comparison of YOLOv3, YOLOv4 and YOLOv5 Performance for Detection of Blood Cells," Int. Res. J. Eng. Technol., pp. 4225–4229, 2021.

G. Plastiras, C. Kyrkou, and T. Theocharides, "You Only Look Once: Unified, Real-Time Object Detection," ACM Int. Conf. Proceeding Ser., 2018, doi: 10.1145/3243394.3243692.

Jiang, P. et al. "A Review of Yolo Algorithm Developments," Procedia Computer Science, 199, pp. 1066–1073. 2021 doi: 10.1016/j.procs.2022.01.135.

P. P. Ray, "Internet of things for smart agriculture: Technologies, practices and future direction," J. Ambient Intell. Smart Environ., vol. 9, no. 4, pp. 395–420, 2017, doi: 10.3233/AIS-170440.

Hermawan, I., Arnaldy, D., Agustin, M., Widyono, M. F., Nathanael, D., & Mulyani, M. T. "Low-cost surveillance system using smartphone and raspberry Pi 4 based on real time streaming Protocol". In 2022 5th International Conference on Computer and Informatics Engineering (IC2IE). IEEE. 2022.

A. D. Boursianis et al., "Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review," Internet of Things (Netherlands), vol. 18, p. 100187, 2022, doi: 10.1016/j.iot.2020.100187.

S. K. Bhoi et al., "An Internet of Things assisted Unmanned Aerial Vehicle based artificial intelligence model for rice pest detection," Microprocess. Microsyst., vol. 80, no. December 2020, p. 103607, 2021, doi: 10.1016/j.micpro.2020.103607.

D. Zhang, X. Zhou, J. Zhang, Y. Lan, C. Xu, and D. Liang, "Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging," PLoS One, vol. 13, no. 5, pp. 1–14, 2018, doi: 10.1371/journal.pone.0187470.

Sari, M. Y. A. et al. "Monitoring Rice Crop and Paddy Field Condition Using UAV RGB Imagery," International Journal on Informatics Visualization, 5(4), pp. 469–474, 2021. doi: 10.30630/JOIV.5.4.742.

Haque, M. E. et al. "Rice Leaf Disease Classification and Detection Using YOLOv5," arXiv 2209.01579, pp. 1–16, 2022.

Li, D. et al. "A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network," Sensors (Switzerland), 20(3), 2020. doi: 10.3390/s20030578.

Hermawan, I., Arnaldy, D., Agustin, M., Widyono, M. F., Nathanael, D., & Mulyani, M. T."Sistem Pengenalan Benih Padi Menggunakan Metode Light Convolutional Neural Network Pada Raspberry Pi 4 B". Jurnal Teknologi Terpadu. Jurnal Teknologi Terpadu Vol, 7(2), 120-126, 2021.

"Rice Diseases Image Dataset | Kaggle." (accessed Oct. 31, 2022).

N. Rao et al., "Analysis of the effect of QoS on video conferencing QoE," 2019 15th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2019, pp. 1267–1272, 2019, doi: 10.1109/IWCMC.2019.8766591.

A. Eko, S. Putro, H. Tolle, and A. P. Kharisma, “Rancang Bangun Aplikasi Penawaran dan Pencarian Kerja Paruh Waktu (Part Time) Berbasis Lokasi,†J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 8, pp. 2548–964, 2018.

G. Ardiansa and R. Primananda, “Manajemen Bandwidth dan Manajemen Pengguna pada Jaringan Wireless Mesh Network dengan Mikrotik,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 1, no. 11, p. 47, 2017.

Subektiningsih, Renaldi dan Ferdiansyah, P. (2022) “Analisis Perbandingan Parameter QoS Standar TIPHON Pada Jaringan Nirkabel Dalam Penerapan Metode PCQ,†Explore, 12(1), hal. 57–63.

Aprianto Budiman, M. Ficky Duskarnaen, and Hamidillah Ajie, “Analisis Quality of Service (Qos) Pada Jaringan Internet Smk Negeri 7 Jakarta,†PINTER J. Pendidik. Tek. Inform. dan Komput., vol. 4, no. 2, pp. 32–36, 2020, doi: 10.21009/pinter.4.2.6.

R. D. Christian, “Sistem monitoring tingkat pencemaran air sungai menggunakan protokol websocket dan modul komunikasi lora,†2021.

N. Eka Budiyanta, M. Mulyadi, and H. Tanudjaja, “Sistem Deteksi Kemurnian Beras berbasis Computer Vision dengan Pendekatan Algoritma YOLO,†J. Inform. J. Pengemb. IT, vol. 6, no. 1, pp. 51–55, 2021.

E. Tirtana, K. Gunadi, and I. Sugiarto, “Penerapan Metode YOLO dan Tesseract-OCR untuk Pendataan Plat Nomor Kendaraan Bermotor Umum di Indonesia Menggunakan Raspberry Pi,†J. Infra, vol. 9, no. 2, p. 7, 2021.

Fajrianti, E. D. et al. "High-Performance Computing on Agriculture: Analysis of Corn Leaf Disease," International Journal on Informatics Visualization, 6(2), pp. 411–417, 2021. doi: 10.30630/joiv.6.2.793.


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