Abstract:
"Waste pollution in Sri Lanka's coastal regions poses a significant challenge, affecting marine
ecosystems, residents, tourism, and the environment. Addressing this issue necessitates innovative
solutions to enhance sustainability efforts and optimize waste management processes.
This research utilizes machine learning techniques to address the pressing need for effective waste
classification. By leveraging convolutional neural networks (CNNs), the proposed solution aims
to automate waste identification and categorization, thereby improving waste sorting practices.
The methodology involves developing and training a CNN model using preprocessed drone-captured waste images. The MobileNetV2 model architecture, stripped of its top layer, is
augmented with custom layers for global average pooling and a dense (output) layer, tailored to
the specific classification task. The model achieves an accuracy of 80.74%, indicating its
effectiveness in correctly classifying waste instances.
However, the research faced limitations due to limited data availability and varying image data
quality, impacting model robustness. The scope was further limited to generating classification
reports rather than implementing real-time alerts, affecting immediate responsiveness. Future
enhancements include implementing multiple object detection, integrating real-time reporting
functionality, and incorporating geolocation mapping for enhanced waste management."
