## 2 Background and Related Work  
Machine learning (ML) and deep learning (DL) methods are subsets of AI techniques. The fundamental distinction between ML and DL approaches is feature selection. In general, ML approaches require handcrafted features (i.e., features created by human experts or retrieved using various techniques but before training), while DL methods automatically extract these features using convolution operations during training. The concept of machine learning may be further segmented into two types: supervised and unsupervised. Supervised learning is a method in which the supplied dataset is labeled, i.e., the expected output is given, while unsupervised learning is a method in which the given dataset is unlabeled [28, 42]. In the recent literature, a large number of DL algorithms using CNNs, transfer learning, and computer vision-based methods have been proposed for the classification of images; however, they are used mainly for biological image data [43, 44, 45]. In contrast to this, there are some DL algorithms using CNNs, and transfer learning methods have been proposed for the plant domain, but this method either works only on a plant leaf, which has a different structure, or has limitations in real-time processing and scalability for plants that have a similar leaf structure. Numerous methods have been developed to classify plants automatically based on their leaves [38, 46]. All ML models require features before the model starts training; therefore, we call these features handcrafted features. This type of feature is extracted from leaves or other portions of plants and categorized as shape features, morphological features, color features, and texture features [36]. Table 1 gives details about the different feature extraction techniques for various ML models for the classification of plants based on a leaf. Figure 1: The overall architecture of the bamboo classification system. The unconstrained categorization and identification of bamboo are challenging in locations where there is a change in luminance, background confusion among various bamboo species and other plants, and local weather conditions. Another obstacle to correct species classification is the similarity of bamboo species in terms of culm body, culm texture, and culm color. There have been numerous attempts to classify bamboo; some have used machine learning and deep learning. Experiments have been carried out to assess fine-grained categorization among a collection of visually extremely similar plant species. The constructed classifier had a 90% accuracy rate in distinguishing between 30 distinct _Chetopodiaceae_ species [51; 52]. [PERSON] et al. [26] proposed an ML-based expert system for bamboo identification using the K-nearest neighbors (KNN) algorithm. The proposed method used 34 characteristics of the shoot, culm (height, diameter, and internode length), sheaths (long, broad, and hair color), branches, and leaves (length and width). The model's name is Case-Based Reasoning (CBR) and it has four main steps: retrieve, reuse, revise, and retain. In another work, [PERSON] et al. [37] presented an image and morphological-based optimized random forest classification method to recognize Indian oxygen plants using SVM, RF, and multilayer perceptron (MLP) classifiers. [PERSON] et al. [53] also performed bamboo shoot species classification using the capability of near-infrared reflectance (NIR) spectroscopy and three ML algorithms, which was less expensive and more time-efficient. Three classification models, i.e., SVM, RF, and partial least squares discriminant analysis (PLS-DA), were used. The SVM classifier demonstrated the most significant classification accuracy, reaching 95% in some cases [53]. Contrary to obsolete machine learning techniques, convolutional neural networks (CNN) may swiftly infer more advanced characteristics from given data. Using DL methods, the model itself extracts the appropriate characteristics [40; 54].