## 2 Materials and Methods
### Related Work  
In paper [6], the authors introduce a sophisticated model integrating computer vision and deep learning for image classification purposes. This innovative approach employs mounted cameras on bridges overlooking rivers to gather visual data, harnesses crowdsourcing for the manual labeling of plastic materials, and develops a two-stage deep neural network structure. Utilizing Faster R-CNN and Inception V2 models to identify and classify plastic objects, the research method evaluates model performance through cross-validation and compares the outcomes with human visual counts. In the study, 1272 JPEG images captured from bridges over rivers at 5 locations in Jakartz, Indonesia, were used to train a segmentation model, and then 14,968 images of floating objects labeled as plastic or not by crowdsourcing were used to train a classification model. The base plastic detection model achieved 59.4% precision on the test set without optimization. Applying data augmentation, an Adam optimizer, and learning rate decay improved the precision to 68.7%. Extrapolation indicated further gains of up to 73% precision with more training data. When tested on new locations, the precision dropped to 20-54% without retraining. Retraining with just 50 new examples improved the precision to 42% on a challenging new site. On 82 samples, the model counts showed reasonable agreement with human visual counts (coefficient of determination R2 = 0.43). The model detected 35% more plastics, on average, compared to human counters. A group of researchers [14] investigated the correlation between the total nitrogen (TN) concentrations extracted from drone-captured hyperspectral imagery of plants and experimentally measured TN concentrations in the Ebinur Lake Oasis. The research utilized four machine learning models, namely, PLS, RF, ELM, and GP, to assess Pearson's Correlation Coefficients and identify significant wavelengths in the red-edge regions. By implementing a fractional-order derivative model, the correlation between the variables improved, achieving an absolute PCC value of 0.63. Then, 45 water samples were collected from sites along the Aqikesu River in Ebinur Lake, China, and used to measure the total nitrogen (TN) concentration in the lab to develop a ground truth dataset. Hyperspectral imagery was captured over sampling sites using a drone-mounted Headwall Nano-Hyperspectral camera (400-1000 nm range). Thirty samples were selected via the Kennard-Stone algorithm for model training. The remaining 15 samples were reserved for model testing. Then, 200 datasets of 45 samples were generated via sampling with replacement from the original 45 used to train the ML models. A fractional-order derivative (FOD) was used to enhance spectral features. The FOD increased the correlation between plant spectra and TN concentration. In addition, 23 sensitive wavelengths were identified using FOD-processed spectra. Four ML models were trained on bootstrapped samples to estimate TN: Random Forest (RF), partial least squares (PLS), Gaussian Process (GP), and extreme learning machines (ELM). The GP model performed best individually, with a coefficient of determination R2 of 0.88 and mean square error (MSE) of 0.52 mg/L on calibration data. All individual models showed high variability in terms of accuracy metrics. The decision-level fusion (DLF) model achieved the highest accuracy (R2 = 0.99) by combining ML models. DLF reduced the uncertainty and minimized the biases of individual models. The validation results also showed high accuracy for DLF (R2 = 0.84, RMSE = 0.5 mg/L). Another group of researchers [13] presented a methodology combining image segmentation, classification, and optical flow to detect and track marine oil spills using aerial imagery. Despite its limited scope, the study demonstrated the potential of deep learning techniques for environmental monitoring and response. Enriching the training data and broadening pollution types could bolster adoption.