Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing
Abstract
:1. Introduction
2. Literature Review
- Single-task focus: A lot of recommended frameworks are made to perform just one thing, like keep track of trash levels, and do not have any features that alert administrators with respect to important events or problems.
- Limited communication range: Frameworks that depend on protocols such as Wi-Fi could face constraints when it comes to short-range data transmission, which could limit their usefulness in bigger areas or outdoor settings.
- Sparse datasets: Most of the proposed techniques struggle with sparse datasets that have few classes for trash classification. The absence of data makes it challenging to provide useful information and identify trash accurately.
- Recycling inadequacy: In certain frameworks, the urgent problem of recycling is not sufficiently handled because of insufficient trash classification and categorization. This shortfall puts the environmental problems related to waste processing at risk of getting worse.
3. Proposed Methodology
3.1. Framework Model Design
3.2. Object Detection Model
3.3. Single-Shot Multibox Detector
Algorithm 1: SSD | |
1. | (input_shape, num_classes) // Define the SSD function with parameters |
2. | base_model = MoblieNetV2() //instantiate MobileNetV2 with provided input_shape, excluding top layers |
3. | For loop each layer in base_model.layer set layer.trainable = false //Set all layers in base_model to non-trainable |
4. | feature_layers = [selected output layers from base_model] |
5. | conv_layers = [] |
6. | For loop over each layer in the feature.layer |
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7. | set prediction_layers = [] //Initialize prediction_layers as an empty list |
8. | For Loop over each conv_layer in conv_layers: |
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9. | return final_prediction // Return the concatenated prediction_layers |
3.4. Waste Classification and Categorization Framework
3.5. Bin Status Monitoring and Locker System
3.6. Experimental Setup
4. Results and Analysis
4.1. Metrics
4.2. Classification Accuracy
5. Conclusions and Future Scope
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author(s) | References | Methodology | Software Used | Benefits | Limitations |
---|---|---|---|---|---|
Aasim et al. 2018 | [24] | Implementation of GSM electronic monitoring system with ultrasonic sensors to detect trash level and notify authorities via SMS. | Ultrasonic sensors and GSM SIM900 | Efficient garbage collection; timely alerts authorities. | Limited ability to accurately determine remaining room in the trash can. |
Misra et al. 2018 | [25] | Utilizing ultrasonic sensors for garbage detection, sending data to a server for analysis and decision making, and forecasting future conditions. | Ultrasonic sensors and cloud server Arduino Integrated Development Environment (IDE) version 1.8.5: | Cloud-monitored waste processing, enhanced decision making, and predictive analytics. System can forecast future conditions and make inferences beyond daily trash amount. | Inability to classify different types of garbage. |
M. Cechecci et al. 2018 | [26] | Low-power sensor node architecture utilizing microprocessor, ultrasonic sensor, and LoRa for data transmission, with emphasis on energy-saving technologies. | Single-chip microprocessor: Arduino Integrated Development Environment (IDE) version 1.6.12., ultrasonic sensor, and LoRa module | Power-saving waste processing; LoRa technology for long-distance communication. Focuses on energy-saving technologies and regulations. | Inability to automatically classify garbage; potential mixed biodegradable/non-biodegradable trash. |
Bobulski et al. 2019 | [20] | CNN-based waste classification system for plastic trash segregation with improved recycling efficiency. | Convolutional Neural Network (CNN): TensorFlow version 1.14. | Automated material sorting, cost reduction, and increased recycling efficiency. | Difficulty in extracting characteristics from images with less depth with networks. |
Nowakowski et al. 2020 | [17] | Image recognition system for identifying and categorizing electronic waste from images utilizing CNN and Faster R-CNN. | Faster R-CNN and Convolutional Neural Network (CNN): TensorFlow version 2.1. | Enhanced waste classification; improved waste pickup planning. Offers both mobile app and server options for image recognition system. | Slower detection performance with large datasets; time-consuming training. |
G White et al. 2020 | [27] | Deep neural network model for waste categorization, deployed at the edge for smart bins, which utilizes transfer learning for improved accuracy. | Jetson Nano edge device: TensorFlow version 2.2. | High accuracy in waste categorization; deployment at the edge for efficiency. | Transfer learning speeds up training but requires initial models from ImageNet. |
Adedeji et al. 2019 | [19] | ResNet-50 extractor combined with SVM for trash classification into glass, metal, paper, and plastic categories, achieving 87% accuracy. | ResNet-50 and Support Vector Machines (SVMs): TensorFlow version 1.12. | Enhanced waste classification; high accuracy. | Limited to specific waste categories; potential difficulty in generalization. |
Wei-Lung et al. 2021 | [28] | CNN-based trash categorization with data augmentation for dataset diversity; optimization of DenseNet 121 using Genetic Algorithm for accuracy improvement. | Convolutional Neural Network (CNN) and Genetic Algorithm (GA): TensorFlow version 2.3. | Increased accuracy in recycling waste categorization; optimization of neural networks. | Time-consuming training; potential overfitting with data augmentation. |
Kellow Pardini et al. 2020 | [21] | The study proposes an IoT-based waste processing framework involving real prototype deployment and a case study. The system includes smart bins equipped with various sensors (HC-SR04, load cell, DHT11, and GPS) and uses Arduino for control. Data are transferred via a SIM900 GSM/GPRS module and integrated with In.IoT middleware. | Arduino IDE, In.IoT middleware, My Waste App (built with Ionic), and Web browser interface: Arduino Integrated Development Environment (IDE) version 1.8.12. | Optimized waste processing, cost savings, environmental benefits, enhanced user interaction, accurate waste detection, efficient route planning, and statistical data generation. | Scalability, cost issues, and lack of battery energy level visualization. |
Component | Quantity | Power Consumed (in Watts) |
---|---|---|
Raspberry Pi 4 | 1 | 15 W |
Servo motor (SG90) | 9 | 9 W |
Pi camera | 1 | 1.25 W–1.5 W |
Ultrasonic sensor | 1 | 0.075 W |
Method | mAP | Batch Size | FPS | #Boxes | Input Resolution |
---|---|---|---|---|---|
Fast YOLO | 52.7 | 1 | 155 | 98 | 448 × 448 |
Faster R-CNN (VGG–16) | 73.2 | 1 | 7 | ~6000 | ~1000 × 600 |
YOLO | 66.4 | 1 | 21 | 98 | 448 × 448 |
SSD512 | 76.8 | 1 | 19 | 24,564 | 512 × 512 |
SSD300 | 74.3 | 1 | 46 | 8732 | 300 × 300 |
Network | Top 1 | Params | Multiply-Add | CPU |
---|---|---|---|---|
MobilenetV2 (1.4) | 74.7 | 6.9 M | 585 M | 143 ms |
ShuffleNet (1.5) | 71.5 | 3.4 M | 292 M | - |
NasNet–A | 74 | 5.3 M | 564 M | 183 ms |
MobilenetV1 | 70.6 | 4.2 M | 575 M | 113 ms |
MobilenetV2 | 72 | 3.4 M | 300 M | 75 ms |
ShuffleNet (x2) | 73.7 | 5.4 M | 524 M | - |
Used Components | Total |
---|---|
Raspberry Pi 4 | 1 |
11.1 V Li-Po battery | 1 |
SG-90 servo motor | 9 |
Pi Camera V2 | 1 |
HC-SR04 ultrasonic sensor | 1 |
Servo driver HAT | 1 |
Trash Compartment | Type of Trash |
---|---|
1 | Glass (green, brown, and white) |
2 | Plastic |
3 | Biological trash |
4 | Metal |
5 | Clothes |
6 | Shoes |
7 | Paper and cardboard |
8 | Non-detectable trash |
No. | Ref. | Object Detection Model | Type of Waste Detectable | Microcontroller Used | Communication Protocol | Sensor Used | Accuracy |
---|---|---|---|---|---|---|---|
1 | [24] | - | Common waste | Arduino Uno and Node MCU | GSM | Ultrasonic Sensor and DHT11 Sensor | - |
2 | [25] | - | Common waste | Arduino Pro Mini | WiFi | Ultrasonic Sensor, Stinky gas Sensor, MQ-135, and MQ-136 | - |
3 | [20] | Modified AlexNet | Plastic | - | - | - | 91% |
4 | [17] | Faster R-CNN | Refrigerators, washing machines, and monitors | - | - | - | 90–96.7% |
5 | [27] | WasteNet | Paper, glass, metal, and cardboard. | - | - | - | 92–94.5% |
6 | Proposed system | Quantized SSD MobileNetV2 | Battery, biological waste, glass (brown, green, and white), clothes, cardboard, plastic, paper, shoes, and trash | Raspberry Pi 4 and Arduino Uno R3 | LoRa | Ultrasonic Sensor and RFID Reader | 93.97% |
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Gude, D.K.; Bandari, H.; Challa, A.K.R.; Tasneem, S.; Tasneem, Z.; Bhattacharjee, S.B.; Lalit, M.; Flores, M.A.L.; Goyal, N. Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing. Sustainability 2024, 16, 7626. https://doi.org/10.3390/su16177626
Gude DK, Bandari H, Challa AKR, Tasneem S, Tasneem Z, Bhattacharjee SB, Lalit M, Flores MAL, Goyal N. Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing. Sustainability. 2024; 16(17):7626. https://doi.org/10.3390/su16177626
Chicago/Turabian StyleGude, Dhanvanth Kumar, Harshavardan Bandari, Anjani Kumar Reddy Challa, Sabiha Tasneem, Zarin Tasneem, Shyama Barna Bhattacharjee, Mohit Lalit, Miguel Angel López Flores, and Nitin Goyal. 2024. "Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing" Sustainability 16, no. 17: 7626. https://doi.org/10.3390/su16177626