# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 1 Introduction
### Image-Based Machine Learning: Application to Aquatic Weed Identification  
Automated plant identification, which relies on photographic images, is a well-developed methodology for agricultural applications. With recent advances in artificial intelligence, several machine learning algorithms have been developed for image classification. Current identification/classification methods involve image preprocessing followed by feature extraction. The extracted features, which typically include the color, shape and texture of leaves, Histograms of Oriented Gradients (HOG), etc., are then used to train classifiers. This approach was applied to identify certain _Ficus_ plant species by acquiring photographic images of leaves, performing the image preprocessing operations shown in Figure 2, and extracting features from the modified images [14]. These authors separately implemented a support vector machine (SVM) and artificial neural network (ANN) as classifiers. A similar approach was adopted by another group of researchers to identify aquatic weeds growing on the water surface, namely _Eichhornia crassipes_, _Pistia stratiotes_, and _Salvinia auriculata_[15]. Visible light images captured from unmanned aerial vehicles (UAVs) were utilized to train multiple classifiers. In addition to SVM, the optimum-path forest classifier (OPF), and Bayesian classifier were investigated. In similar research, feature learning on high resolution weed images (water hyacinth, tropical soda apple, and serrated tussock) was performed using a filter bank, followed by image classification via the texton approach (K-means clustering) [16]. Feature extraction and OPF classifiers were successfully implemented in another study to identify invasive yellow flag iris plants [17]. Despite the documented success of these feature extraction-based plant classification methods, they cannot be directly applied to subsurface plant identification due to limited visibility and the associated lack of photographic clarity. Despite lacking the resolution and clarity of photography, hydroacoustic imaging is an emerging option for the quantification of underwater vegetation. Currently, hydroacoustic data files can be uploaded and processed offline via a web-based service (Navico BioBase), which generates biomass-concentration maps. However, the limited resolution of hydroacoustic data impedes feature extraction and typical machine learning approaches to species classification. Deep learning is a more advanced machine learning technique that could overcome these limitations [18]. It employs deep neural networks (DNNs) to simultaneously accomplish feature extraction and classification tasks [19]. The fundamental component of a DNN is an artificial neural network (ANN). As shown in Figure 3, a standard feedforward ANN consists of multiple fully connected layers (input, hidden, and output layers); each layer consists of a varying number of nodes (artificial neurons) depending on the complexity of the network. The input layer \(X=[x_{1},x_{2},\ldots x_{n}]^{T}\) takes \(n\) extracted features, which are multiplied by an adaptable weight matrix \(W^{1}=[w^{1}_{11},w^{1}_{12},\ldots,w^{1}_{1h}\cdot\ldots;w^{1}_{n1},\ldots,w^ {1}_{nh}]\), where \(h\) is the number of neurons in the hidden layer. These values are multiplied by another adaptable weight matrix \(W^{2}=[w^{2}_{11},w^{2}_{12},\ldots,w^{2}_{1m^{2}}\cdot\ldots;w^{2}_{h1}, \ldots,w^{2}_{hm}]\), where \(m\) is the number of ANN outputs \(B^{2}\). Each ANN output corresponds to a specific classification (\(m\) classes in this case). By incorporating non-linear activation functions in the output layer neurons (e.g., softmax or sigmoid), the output layer values can  
Figure 2: Extraction from an image of a leaf (adapted from [4]): (**a**) original photograph; (**b**) conversion from RGB to HSV format to serve as a guide for subsequent edge detection; (**c**) grayscale conversion to improve image contrast; and (**d**) feature extraction after identifying leaf boundaries.