Identification Accuracy of Cost-Effective Visible (VIS) to Near-Infrared (NIR) Sensor AS7265x on Plastic Waste in the Philippines
Document Types
Paper Presentation
School Name
De La Salle University, Manila
Track or Strand
Science, Technology, Engineering, and Mathematics (STEM)
Research Advisor (Last Name, First Name, Middle Initial)
Cu, Gregory G.
Start Date
25-6-2025 10:30 AM
End Date
25-6-2025 12:00 PM
Zoom Link/ Room Assignment
https://zoom.us/j/99613886879?pwd=XPy80hbLCUaaWCllnM7yHo2WN7kquy.1 Meeting ID: 996 1388 6879 Passcode: 259997
Abstract/Executive Summary
In the Philippines, plastic waste persists as a significant issue caused by the widespread use of single-use plastics and the lack of recycling systems. The lack of proper systems made waste segregation and identification inefficient and disorganized. Past studies explain the use of VIS/NIR in plastic segregation and potential of spectral frequencies. However, there is limited investigation of the AS7265x sensor’s consistency when integrated with machine learning to further improve detection reliability; thus, the study evaluates the effectiveness of the AS7265x triad spectroscopy sensor, incorporated with machine learning algorithms. An experimental setup was created using the AS7265x sensor and Arduino IDE to capture wavelength patterns from six plastic types within a controlled light-proof bin. Collected data were then processed using WEKA software with machine learning classifiers Naive Baynes, Multilayer Perceptron, sequential minimal optimization (SMO), IBk (k-Nearest Neighbor), J48, and Random Forest to identify plastics according to their wavelength patterns. The Random Forest model yielded the highest accuracy of 92.06%, while other models showed lower performance. The findings suggest that the Random Forest model is able to analyze intricate, and high-dimensional spectral inputs by ensemble learning, enabling it to categorize plastic types despite their subtle differences. Other lower-performing models, however, have shown to struggle with non-linear connections and feature correlations. The machine learning-integrated sensor performed at varying levels; therefore, there is a need to explore and select the best-fitting model in real-world settings to achieve optimal accuracy in plastic identification.
Keywords
plastic waste identification; machine learning; AS7265x sensor; WEKA Workbench; Random Forest
Research Theme (for Paper Presentation and Poster Presentation submissions only)
Computer and Software Technology, and Robotics (CSR)
Initial Consent for Publication
yes
Statement of Originality
yes
Identification Accuracy of Cost-Effective Visible (VIS) to Near-Infrared (NIR) Sensor AS7265x on Plastic Waste in the Philippines
In the Philippines, plastic waste persists as a significant issue caused by the widespread use of single-use plastics and the lack of recycling systems. The lack of proper systems made waste segregation and identification inefficient and disorganized. Past studies explain the use of VIS/NIR in plastic segregation and potential of spectral frequencies. However, there is limited investigation of the AS7265x sensor’s consistency when integrated with machine learning to further improve detection reliability; thus, the study evaluates the effectiveness of the AS7265x triad spectroscopy sensor, incorporated with machine learning algorithms. An experimental setup was created using the AS7265x sensor and Arduino IDE to capture wavelength patterns from six plastic types within a controlled light-proof bin. Collected data were then processed using WEKA software with machine learning classifiers Naive Baynes, Multilayer Perceptron, sequential minimal optimization (SMO), IBk (k-Nearest Neighbor), J48, and Random Forest to identify plastics according to their wavelength patterns. The Random Forest model yielded the highest accuracy of 92.06%, while other models showed lower performance. The findings suggest that the Random Forest model is able to analyze intricate, and high-dimensional spectral inputs by ensemble learning, enabling it to categorize plastic types despite their subtle differences. Other lower-performing models, however, have shown to struggle with non-linear connections and feature correlations. The machine learning-integrated sensor performed at varying levels; therefore, there is a need to explore and select the best-fitting model in real-world settings to achieve optimal accuracy in plastic identification.
https://animorepository.dlsu.edu.ph/conf_shsrescon/2025/paper_csr/12
