Smart Waste Management and Classification Systems: Comparison
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Waste management requires necessary processes and activities to dominate from its inception to demolition. Waste comes in solid, liquid, or gaseous form, and every type of waste demands a different method of classification, disposal, and management. Waste management deals with every waste category, including household, organic, industrial, municipal, biomedical, organic, biological, and radioactive waste. Any unnecessary substance or substance with no use is called “waste”. Waste management involves the collection of the waste and its transport and disposal to appropriate locations. In the European Union (EUROPA), 423 million tons or 56% of domestic waste was recycled in 2016. Reports reflect the need for proper household waste management for the recycling process. Most of the Earth’s population will emigrate from rural to urban areas in the coming years. Therefore, bigger cities will require a highly sustainable infrastructure and smart waste management system to fulfill the fundamental needs of its citizens and provide them with a good service for the future.

  • waste classification
  • waste management
  • image recognition
  • smart city
  • smart environments
  • convolutional neural network (CNN)
  • internet of things (IoT)
  • deep learning (DL)
  • sustainability and environment

1. Introduction

Waste management requires necessary processes and activities to dominate from its inception to demolition. Waste comes in solid, liquid, or gaseous form, and every type of waste demands a different method of classification, disposal, and management. Waste management deals with every waste category, including household, organic, industrial, municipal, biomedical, organic, biological, and radioactive waste. Any unnecessary substance or substance with no use is called “waste”. Waste management involves the collection of the waste [1] and its transport and disposal to appropriate locations [2]. In the European Union (EUROPA), 423 million tons or 56% of domestic waste was recycled in 2016. Reports reflect the need for proper household waste management for the recycling process [3]. According to [4], most of the Earth’s population will emigrate from rural to urban areas in the coming years. Therefore, bigger cities will require a highly sustainable infrastructure and smart waste management system to fulfill the fundamental needs of its citizens and provide them with a good service for the future [5][6].
Traditional recycling processes segregate waste objects manually or by applying a sequence of filters. If modern technology and waste management could be bound together, the results would be immeasurable and would lead to a positive biological environment [7]. With the rapid increase in computing power, there has been a lot of advancement in image processing [8] and computer vision [9]. A deep-learning architecture named convolutional neural network (CNN) has played a pivotal role in this regard [8]. By applying deep learning, waste objects can be identified and classified more efficiently, reducing the cost in terms of both time and human resources, and impacting the environment positively [10][11].
According to an estimation by FUSON [12], 127 new devices are connected to public networks every second. Given this speedy growth, 328 million new devices are added monthly. According to STATISTA, at the end of 2023, the IoT market will be projected to be worth $1.1 trillion [13]. These statistics suggest that IoT is becoming a significant element in modern computing techniques. In the modern web, the Internet of Things (IoT) [14], Machine-Learning (ML) [15], and Deep-Learning (DL) [14] phenomena are being enabled in various systems such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID) [16], sensors, and actuators. Prediction methods such as clustering and classification [17] are also used to create the most accurate results instead of individuals.
Numerous types of sensors are used in this project. Their purpose is to gather information about waste material and thus enhance the city’s infrastructure by successfully implementing waste management and classification tasks. The physical infrastructure of outhe researchers' system consists of waste bins, a fleet of vehicles, gripper, dump, etc. First, the household waste is collected in our the researchers' smart waste bin, whose data are stored on the cloud, and a message on the web/mobile application is generated when the bin gets full. Afterward, the authorities assign a waste collection truck to collect the waste from the waste bin and take it to the dumping area of the waste, where the segmentation and classification of waste are performed as shown in Figure 1.
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Figure 1. Smart Waste Classification Mechanism.

2. Smart Waste Management and Classification Systems

With the rapid modernization of every sector of society, nowadays, people rely on technology for everything. In this growing age, human lives have changed a lot, and modern technology has taken its place in the heart of every human being. Undoubtedly, there is no field in outhe researchers' surroundings where technology does not play a vital role. People prefer to live in cities with the latest facilities and technology. As a result, the population in cities is increasing daily, which has many disadvantages and advantages [18][19]. Individuals working in cities have a positive effect on the economy of the country [20][21]. Still, as societies become increasingly congested, many problems related to health, safety, and the environment arise [22][23][24][25]. These problems include medical facilities, security, privacy, and transportation [26][27].
Another huge problem in cities nowadays is waste management, which involves the collection, transportation, and classification of waste, and also helps with recycling waste items [27][28]. Intensive usage of natural resources has become unavoidable [29]. In contrast, due to increasing consumption trends, waste objects have reached levels that endanger human health and the environment in quantity and harmful content. Chemical, manufacturing, physical, and consumption properties are the considerations used to classify waste items [30][31]. Most of the population of growing cities is educated and well aware of the environmental effects of waste, but they dispose of their waste without classifying it. Everything in this universe has two aspects; one is good, and one is bad. Man feels this when it happens to him, or his belongings [3]. Several countries have placed bins with separate compartments for different waste categories. Still, the dwellers are not following the rules, which makes waste management and classification a complex task that needs a specific system to be designed to perform waste classification automatically [32][33].
According to [34][35][36][37], it has been proved that nearly 0.75% of solid domestic waste can be recycled. Therefore, it will be costly to dump it and not recycle it. If these waste items are classified, it will boost the recycling process, which will positively affect the economic boost of the country [38]. It will also provide a much greener environment for future generations to live in [39]. In short, failing to prioritize recycling can cause wastage of natural resources, and financial loss [40][41]. Recycling is a viable solution, though it can be daunting to classify waste accurately. The efficient management of waste has a significant impact on people’s quality of life. The reason is that waste disposal has a clear connection with adverse effects on the environment, and thus, people’s health. Therefore, there is a need for a proper plan for a waste management system for the betterment of the people who want to live in a healthy environment [42][43].
Various countries such as America, Canada, Russia, Italy, Malaysia, the Kingdom of Saudi Arabia, Qatar, etc., and many other countries are working to develop a smart waste management system. In previous work, the waste management technique which was implemented in St. Petersburg, Russia, used Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID) [44], sensors, and actuators. St. Petersburg is a city of 5 million individuals [45]. On average, 1.7 million tons of solid waste is produced in the city annually. Whereas, in Canada [46] the k-means and linear regression are used for waste management systems, in which multiple beats are involved to regulate the cycle.
In [47], single waste image classification was performed using SVM with SIFT and CNN. They manually collected a total of 2527 waste images. SVM and CNN models were trained on the collected dataset and achieved 63% and 22% accuracy, respectively. Their research classifies waste into six categories; paper, metal, cardboard, plastic, glass, and trash [48]. Municipal solid waste can be classified into either six or four categories. In six-class systems [49][50][51][52] researchers focus on recyclable waste classes: paper, metal, glass, cardboard, plastic, and trash. In four-class systems [53][54][55][56][57] waste classes are wet waste, i.e., probably kitchen waste, dry garbage, recyclable and hazardous garbage. A ResNet-5013 model and SVM-based intelligent system was proposed in [58]. The system was tested on a single waste images dataset [53] and gained 87% accuracy in classification. In public places for automatic detection of recyclable wastes, a multilayered hybrid deep-learning-based system was proposed [59]. The system was employed with CNN for image features extraction and a multilayered perceptron used for consolidating only relevant features of the image. Their proposed technique outperforms and achieves an accuracy of 90% for classification. A CNN-based system was developed to classify plastic wastes [60].
For Municipal Solid Waste (MSW), derived classifier models [61] were proposed based on transfer learning. Models were retrained on 9200 MSW images by pertained classifiers of CNN (VGG16, MobileNetV2, ResNet50, and DenseNet121) to classify the waste into four predefined groups (recyclable waste, hazardous waste, compostable waste, and general waste). In [62], the authors proposed an image classifier to identify the waste item and classify its category. In their research, four classifiers of CNN (VGG16, DenseNet169, ResNet50, and AlexNet) trained on the ImageNet dataset were used for feature extraction from waste images to classify them into six categories: paper, metal, cardboard, plastic, glass, and trash. Their results reflect that ResNet50 performs better, and its performance is closer to DenseNet169. The flaw in their proposed mechanism is that it misclassifies glass. Since the ILSVRC Competition, different image classification methods based on CNN architectures have developed [63][64][65]. In image classification of computer vision, VGG16 and VGG19 (also known as VGGNet) are two representatives of CNN architectures, achieving the best performance in the ILSVRC Competition. For large-scale image recognition, these models use 3 × 3 tiny convolutional filters in every layer and push the depth of the network from 16–19 layers.
Recent studies reported that deep learning (DL) models are more effective for object detection and classification than traditional techniques. Due to rapid urbanization, smart cities are being designed with smart and automated waste management using the internet of things (IoT) technologies that lead to an increase in efficiency and flexibility, saving energy and time and keeping the environment sustainable [8][66][67][68][69][70][71]. IoT-based solutions provide real-time monitoring, collecting, and management of garbage. In [72], the authors developed IoT-based smart bins using deep learning (DL) and machine learning (ML) models to monitor, collect, manage waste, and forecast air pollutants present in the surrounding environment. An IoT stationed smart waste segregation and management system was developed by Shamin et al. [73] employed with an ultrasonic sensor, a moisture sensor, a metal sensor, and a camera. Image processing and machine learning algorithms were used to identify degradable waste items and segregate them into different dustbins.
Before proposing a solution of outhe researchers' own, outheir research covered a wide range of previously proposed models, papers, and studies. All the research and studies were thoroughly read and understood, considering their domain of interest, their architecture, the pros and cons, the features added in their studies, and the accuracy of the architecture proposed. After critically evaluating many studies on waste management and classification, some crucial information about these studies is provided in Table 1 and Table 2. So, the readers can have an overview of the previous work carried out by researchers, practitioners, authors, and technologists related to the subject mentioned.
Table 1. Summary of Related Research Efforts 1A.
Study Architecture For IoT Waste Category Hardware Category Accuracy (%) Dataset Remarks
[74][75] Multivariate analysis with neural network NA Light Weight

Metals		 Weight sensor, linear laser, 3D camera 85% NA Cannot classify other materials
Most waste items do not have product labels; hence, the scope is limited
Table 2. Summary of Related Research Efforts 1B.
Study Architecture For IoT Waste Category Hardware Category Accuracy (%) Dataset Remarks
[76
[52] VGG16, CNN, ResNet50 NA Household food waste, recyclable waste, hazardous waste, residual waste NA VGG16: 37% CNN: 37% ResNet-50: 47% Self-made Not integrated with a decision support or classification system. Accuracy is not properly calculated
] KNN NA Recyclable

Paper		 Camera 93% NA Require consistent lightening during identification phase
[82] YOLOv3, Darknet neural network NA Glass, paper, metal, plastic, cardboard, organic waste NA Glass: 97%

paper: 85%

metal: 99%

plastic: 91%

cardboard: 97%

org-waste: 98%

mAP: 94%		 Self-made Takes more time to detect an object. [77] Hu’s image invariant moments with KNN NA Inorganic material such as bottles, cutlery, Cans Camera 98% NA Unreliable results due to small dataset
[83] WasNet, Lightweight neural network NA NA Camera ImageNet: 64.5% Garbage Classification: 82.5% TrashNet: 96.10% ImageNet, Garbage Classification dataset, TrashNet Implementation and results are unreliable. [78]
[DNA Computing Algorithms (RGBI) NA Paper Camera 95.1% NA Require consistent lightening during identification phase
73] SURF algo., KNN Yes Metal, Plastic Ultrasonic sensor, Moister sensor, metal sensor, Camera 95% Self Made Only one waste item can be put in dustbin at a time. If image of waste material captured in low light then results may vary. [79]
[SVM and CNN NA Paper, Metal and Plastic 57] CNN, Self-Learning Neural Network NACamera SVM: 94.8% CNN: 83% NA Dataset is less versatile due to Low GPU memory images scaled down from 256 × 256 to 32 × 32
Plastic, Paper, Cardboard, Metal NA 76% Trash Net Less accuracy due to limited dataset. Not integrated with a decision support or classification system. [80] Fast-R CNN NA Plastic Bottles
[Robotic Arm, KUKA, Camera 8491% NA ]Segregate only plastic. Output reliant on lighting
CNN, Image Processing Algo. NA Plastic and its four categorical: PS, PP, PE-HD, PET Camera, Airjet 74% WaDaBa Less effective as compared to other CNNs. [81] Scale Invariant Feature Transform (SIFT) NA Organic and inorganic items with product labels Camera 89.9%
[62] ResNet50, DenseNet169, VGG16, AlexNet NA Glass, paper, metal, plastic, cardboard, Trash NA ResNet50: 89.7%

DenseNet169: 92.6%

VGG16: 86.9%

AlexNet: 83.7%		Self-made dataset of 192 images ImageNet System misclassify ‘glass’. Not integrated with a decision support or classification system.
Previous studies have their benefits and limitations, and having their results in mind helped the researchers propose a system that can overcome all the limitations. Previous research studies solved the issue of waste classification and management to some extent, but all of them lag one way or the other. Some have combined multiple approaches to propose a hybrid solution. The best-known accuracy has been achieved through the hybrid approach of Deep Learning algorithms: Inception and ResNet. The accuracy achieved was over 88% in classifying waste items.
Similarly, many proposed systems have a hybrid solution consisting of “machine learning and deep learning”. Still, the search for a more accurate and reliable system continues. OurThe research aims to focus on observing and understanding traditional methods for automatic waste classification systems, which can further help in the recycling process of waste items. Currently, many techniques for waste classification exist, but many require human involvement. If a fully automatic system is deployed in any society, it will be a win–win situation for the government, societies, and industrialists. The underlying purpose of this research is to provide an automated waste management system that can perform classification quickly and provide better and more accurate results at a low cost.

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