# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 3 Results
### Machine Learning Algorithm  
The deep learning algorithm, in conjunction with data augmentation, successfully extracted features and accurately classified underwater vegetation using hydroacoustic imagery. Figure 19 shows classification accuracy as a function of training iterations for both training and validation sets; it clearly illustrates how data augmentation reduced overfitting in the validation sets.  
Initial parameter tuning readily improved the validation accuracy to approximately 97%; further parameter tuning produced only marginal gains. The optimizers'sgdm', 'rmsprop', and 'adam' gave similar performance, with'sgdm' and 'adam' producing marginally better accuracy. Increased smoothness of the training curve and improved accuracy were observed for all  
Figure 19: Training progress (**a**) before and (**b**) following data augmentation. Overfitting in (**a**) is evident through high variation between training and validation accuracy.  
the learning rate. A learning rate drop schedule further improved the accuracy as compared to a constant learning rate. The \"MiniBatch Size\" parameter contributed heavily to runtimes and smoothness of the training curve. Greater batch sizes smoothed the curve and reduced runtimes, but increased memory requirements. Limiting batch sizes to 256 or less helped avoid the \"sharp minimizers\" that tend to impede generalization [30].  
Following parameter tuning, the DNN achieved a classification accuracy of 99.06% for both the validation and test data sets, clearly indicating excellent generalization (_Cabomba_: precision = 1, recall = 1; _Hydrilla_: precision = 0.9873, recall = 0.975; Other: precision = 0.9753, recall = 0.9875). Analysis of the confusion matrices for different tuning parameters revealed more misclassifications within the _Hydrilla_ and Other classes (and between the two) than within the _Cabomba_ and Countail classes. Figure 20 shows the confusion matrices for the optimal parameter set.  
Subsequent analysis revealed that misclassified _Hydrilla_ images tended to be associated with limited plant growth (Figure 21a). The majority of images containing _Cabomba_ and Countail were associated with more mature plant growth, which perhaps led to higher classification accuracies.  
Figure 20: Confusion matrices of classified vegetation species for (**a**) validation and (**b**) test images.  
This suggests that timing of data capture within the growing season might play an important role in classification accuracy. Data for _Hydrilla_ was collected from June to November while _Cabomba_ and Coontail were scanned during December, a time of year associated with more mature growth. Other instances of misclassification included images containing floating vegetation or schools of fish (Figure 21b), and vegetation being only partially contained within the image (Figure 21c).  
Figure 21: Misclassified images due to (**a**) limited vegetation growth, (**b**) floating vegetation or schools of fish, and (**c**) vegetation partially contained within the image.  
During DNN training, weights of all layers undergo continuous optimization. Successful feature learning creates smooth filters with uniform weight gradients. Figure 22 visualizes the 96 filters (each with \(11\times 11\times 3\) weights) of the first convolutional layer for two separate cases. Parameter tuning generated smoother, blended (less discretized) gradient patterns, which are characteristics of effective training (Figure 22b) [31], while filters in Figure 22a indicate lack of sufficient training.