CNN-Based IoT System for River Waste Detection

Authors

  • I Gusti Made Ngurah Desnanjaya Department of Computer System Engineering, Institute of Business and Technology Indonesia, Denpasar, Indonesia
  • I Made Aditya Nugraha Department of Fisheries Mechanization, Marine and Fisheries Polytechnic of Kupang, Kupang, Indonesia
  • I Kadek Adi Widagda Department of Computer System Engineering, Institute of Business and Technology Indonesia, Denpasar, Indonesia
  • I Gede Adnyana Department of Computer System Engineering, Institute of Business and Technology Indonesia, Denpasar, Indonesia

DOI:

https://doi.org/10.58982/krisnadana.v4i2.751

Keywords:

CNN Model, Waste Management, Weight Classification

Abstract

Efficient automated waste management presents a significant challenge in supporting environmental sustainability. This study designed and implemented a weight-based waste transport and classification system using IoT technology and a Convolutional Neural Network (CNN) model. The primary issue addressed was the low efficiency of manual waste handling, particularly for heavier waste types. The system features a motor-driven conveyor, HC-SR04 ultrasonic sensors, and a NodeMCU ESP8266 as the main controller. The CNN model was employed to classify waste into light (<50 grams), medium (50–150 grams), and heavy (>150 grams) categories.Testing revealed a transport success rate of 100% for light waste, 90–95% for medium waste, but dropped to 40% for heavy waste. The CNN model demonstrated high accuracy (>95%) for light waste classification but decreased to <70% for heavy waste due to the complexity of features. In conclusion, the system is effective for light and medium waste but requires optimisation for heavy waste, showing great potential as an innovative solution for sustainable waste management.

References

M. R. Syafari, M. N. I. Ridwan, and A. Anjani, “Waste Management Model of River Beach Communities in Banjarmasin City,” Int. J. Polit. Law, …, vol. 3, no. 3, pp. 1–9, 2022, doi: https://doi.org/10.20527/iis.v4i1.6363.

M. J. Uddin and Y. K. Jeong, “Urban river pollution in Bangladesh during last 40 years: potential public health and ecological risk, present policy, and future prospects toward smart water management,” Heliyon, vol. 7, no. 2. HLY, p. e06107, 2021. doi: 10.1016/j.heliyon.2021.e06107.

F. O. Ajibade et al., Environmental pollution and their socioeconomic impacts, no. December. 2020. doi: 10.1016/B978-0-12-821199-1.00025-0.

N. M. R. Sukmawati, G. Ginaya, I. G. M. Wendri, I. G. A. M. K. Komala, and I. D. G. A. Pemayun, “From Garbage to Profit: Creative Economy and 3R Waste Management System in Tenganan Tourism Village Bali,” Int. J. Soc. Sci. Res. Rev., vol. 5, no. 12, pp. 592–600, 2022, doi: https://doi.org/10.47814/ijssrr.v5i12.870.

F. C. Mihai et al., “Plastic Pollution, Waste Management Issues, and Circular Economy Opportunities in Rural Communities,” Sustain., vol. 14, no. 1, 2022, doi: 10.3390/su14010020.

K. Q. Ain, M. A. Nasri, M. N. Alamsyah, M. D. R. Pratama, and T. Kurniawan, “Collaborative governance in managing plastic waste in Bali,” IOP Conf. Ser. Earth Environ. Sci., vol. 905, no. 1, 2021, doi: 10.1088/1755-1315/905/1/012115.

S. E. Hale, M. S. Folde, U. H. Melby, E. U. Sjødahl, A. B. Smebye, and A. M. P. Oen, “From landfills to landscapes—Nature-based solutions for water management taking into account legacy contamination,” Integr. Environ. Assess. Manag., vol. 18, no. 1, pp. 99–107, 2022, doi: 10.1002/ieam.4467.

N. Abdullah et al., IoT-Based Waste Management System in Formal and Informal Public Areas in Mecca, vol. 19, no. 20. 2022. doi: 10.3390/ijerph192013066.

P. Kanade, P. Alva, J. P. Prasad, and S. Kanade, “Smart Garbage Monitoring System using Internet of Things(IoT),” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. April, pp. 330–335, 2021, doi: 10.1109/ICCMC51019.2021.9418359.

S. Chowdhury et al., “Recent trends of plastic waste management for sustainable environment in Indian context,” Mater. Today Proc., no. June, 2023, doi: 10.1016/j.matpr.2023.06.063.

C. Wang, J. Qin, C. Qu, X. Ran, C. Liu, and B. Chen, “A smart municipal waste management system based on deep-learning and Internet of Things,” Waste Manag., vol. 135, no. November, pp. 20–29, 2021, doi: 10.1016/j.wasman.2021.08.028.

J. Gupta, S. Pathak, and G. Kumar, “Deep Learning (CNN) and Transfer Learning: A Review,” J. Phys. Conf. Ser., vol. 2273, no. 1, 2022, doi: 10.1088/1742-6596/2273/1/012029.

M. Malik et al., “Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models,” Sustain., vol. 14, no. 12, pp. 1–18, 2022, doi: 10.3390/su14127222.

B. Xue, B. Huang, G. Chen, H. Li, and W. Wei, “Deep-sea debris identification using deep convolutional neural networks,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 8909–8921, 2021, doi: 10.1109/JSTARS.2021.3107853.

B. Xue et al., “An Efficient Deep-Sea Debris Detection Method Using Deep Neural Networks,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 12348–12360, 2021, doi: 10.1109/JSTARS.2021.3130238.

I. M. A. Nugraha, I. G. M. N. Desnanjaya, and F. Luthfiani, “Energy and economic prospects from the utilization of sawdust waste as biomass briquettes in East Nusa Tenggara,” Int. J. Power Electron. Drive Syst., vol. 15, no. 1, pp. 540–547, 2024, doi: 10.11591/ijpeds.v15.i1.pp540-547.

N. C. A. Sallang, M. T. Islam, M. S. Islam, and H. Arshad, “A CNN-Based Smart Waste Management System Using TensorFlow Lite and LoRa-GPS Shield in Internet of Things Environment,” IEEE Access, vol. 9, pp. 153560–153574, 2021, doi: 10.1109/ACCESS.2021.3128314.

O. Youme, T. Bayet, J. M. Dembele, and C. Cambier, “Deep Learning and Remote Sensing: Detection of Dumping Waste Using UAV,” Procedia Comput. Sci., vol. 185, no. June, pp. 361–369, 2021, doi: 10.1016/j.procs.2021.05.037.

J. P. Nonso Nnamoko, Joseph Barrowclough, “Solid Waste Image Classification Using Deep Convolutional Neural Network,” infrastructures, vol. 7, no. 47, pp. 1–15, 2022, doi: doi.org/10.3390/infrastructures 7040047.

S. N. S. A. Tarmizi, N. N. S. N. Dzulkefli, R. Abdullah, S. I. Ismail, and S. Omar, “Internet of things-based garbage monitoring system integrated with Telegram,” Indones. J. Electr. Eng. Comput. Sci., vol. 32, no. 3, pp. 1370–1377, 2023, doi: 10.11591/IJEECS.V32.I3.PP1370-1377.

J. John et al., Smart Prediction and Monitoring of Waste Disposal System Using IoT and Cloud for IoT Based Smart Cities, vol. 122, no. 1. 2022. doi: 10.1007/s11277-021-08897-z.

F. F. Putra and Y. D. Prabowo, “Low resource deep learning to detect waste intensity in the river flow,” Bull. Electr. Eng. Informatics, vol. 10, no. 5, pp. 2724–2732, 2021, doi: 10.11591/eei.v10i5.3062.

Downloads

Published

2025-01-31

How to Cite

Desnanjaya, I. G. M. N. ., Nugraha, I. M. A., Widagda, I. K. A. ., & Adnyana, I. G. (2025). CNN-Based IoT System for River Waste Detection . Krisnadana Journal, 4(2), 86-94. https://doi.org/10.58982/krisnadana.v4i2.751