A Smart Recycling Bin for Waste Classification

Ziouzios, Dimitris; Dasygenis, Minas

doi:10.1109/pacet48583.2019.8956270

Cited by 36 publications

(13 citation statements)

References 5 publications

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“…Waste bins that collect and compost food are uncommon for the aforementioned reasons. This research work extends our previous paper [10]. A modernized smart bin; an Internet of Things (IoT) device that utilizes modern technologies like LoRaWAN and environment sensors to assist with the compost process and help citizens recycle food that would otherwise be wasted.…”

Section: Introductionsupporting

confidence: 52%

Envisioning IoT applications in a smart city to underpin an effective municipal strategy: The smartbin project

et al. 2021

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Due to the Earth’s population rapid growth and the modern lifestyle, the urban waste creation rate constantly increases.Organic waste, which forms a significant part of municipal solid waste, has caused increasing environmental concerns. Recycling is the only way to make a sustainable environment. According to estimations from the United States Environmental Protection Agency, around 24 percent of the total waste can be composted. Instead, most of it is land filled and incinerated. By composting organic waste,we can preserve resources and produce a valuable by-product that can be used as a locally produced fertilizer. In this paper, we propose a solution: a low-cost and effective Smart Compost Bin that utilizes modern technologies, such as environmental sensors and the LoRaWAN protocol to assist with the compost process.A centralized Information System collects measurements from smart bins that can be deployed anywhere and can further assist with the waste collection process. We have performed several experiments with our smart bin prototype, evaluated its efficiency and concluded that it is a feasible solution.

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Section: Introductionsupporting

confidence: 52%

Envisioning IoT applications in a smart city to underpin an effective municipal strategy: The smartbin project

et al. 2021

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“…This framework utilizes a convolutional neural organization (CNN) to distinguish and isolate squander into various classes, like metal, glass, paper, and plastic. A sum of 400 to 500 pictures containing the four unique types is utilized to prepare the organization [19].…”

Section: Related Workmentioning

confidence: 99%

Prototype Design Internet of Things Based Waste Management Using Image Processing

Widianto¹,

Wahyu²,

Gusna³

2021

Adv. sci. technol. eng. syst. j.

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Waste is currently a serious problem often found in rural areas, rural areas, and even industrial areas. Waste is a side effect of activities carried out by humans to meet social or industrial needs. Increasing human productivity will also increase the amount of waste produced. To overcome this, a sorting management system is needed. Good waste is seen from the processing method to the recycling process. The waste management problem still relies on the old system transporting and disposing of waste to the final disposal site (TPA). The TPA itself sometimes piles up in one place so that the waste process becomes uneven and the sorting process is not good, causing type waste. This accumulates and mixes with other hazardous waste. In today's modern era, the management and sorting system is the same. Object detection and waste classification are carried out in the Sensor system to introduce the previously prepared model. The prototype article recognition model is prepared with waste images to produce a freeze forecast graph used for object discovery which is carried out via the camera associated with the Arduino Uno as the basic unit handling. Ultrasonic sensors are inserted into each garbage compartment to filter out the refill filling rate. The sorting system itself can use computer-based image processing methods. Image Processing is used to process data in real-time and fill the trash level. The sensor module that is implanted to detect waste management personnel, the results of this study prove that image management can accommodate waste particles and tested in the BlackBox method produces results following the required quantitative with the accuracy in both the camera, sensor, and image process used can detect an average of 70%.

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“…A smart recycling bin is proposed in [29]. The system uses Raspberry Pi 3 and Xilinx PYNQ-Z1 FPGA board together with pretrained ResNet-34, a convolution neural network containing 34 pretrained layers to perform waste classification.…”

Section: Related Workmentioning

confidence: 99%

An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model

et al. 2020

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A Smart Recycling Bin for Waste Classification

Cited by 36 publications

References 5 publications

Envisioning IoT applications in a smart city to underpin an effective municipal strategy: The smartbin project

Envisioning IoT applications in a smart city to underpin an effective municipal strategy: The smartbin project

Prototype Design Internet of Things Based Waste Management Using Image Processing

An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model

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