## 2 Background and Related Work
This approach has been shown to enhance ML approaches for image classification, in addition to relieving the burden of feature selection [55]. Convolution is a well-known technique in the fields of computer vision and signal processing. Professionals in computer vision often use the convolution operation to accomplish noise reduction and edge identification. CNNs are a particular type of artificial neural network (ANN) that has become important in a variety \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Species/Classes** & **Features and Method** & **Classifier** & **Accuracy (\%)** & **Article** \\ \hline Flavia & 32 & Vein features & RF & 90 & [47] \\ Flavia & 32 & Color, texture, vein and shape—GLCM & Radial Basis & 93.80 & [33] \\  & & & Probabilistic Neural & & \\  & & & Network (RBPNN) & & \\  & & & RF with Synthetic & & \\ Real & 63 & Vein and shape & Minority Over- & 91 & [48] \\  & & & Sampling Technique & & \\  & & & (SMOTE) & & \\ Real & 7 & Dimensional and shape & SVM & 93.13 & [28] \\  & & & Multilayer & 94.00 & [49] \\  & & & Perceptron & & \\  & & & (MLP) & & \\ Flavia & 32 & Texture—LBP, GLCM & SVM & 93.8 & [34] \\ ImageClef & 10 & Shape, color, and texture— & SVM & 98 & [50] \\  & & & Scale Invariant Feature & & \\  & & & Transform (SIFT) & & \\ Flavia & 32 & Shape, texture, and color— & KNN & 98.8 & [38] \\  & & & Features (DMFs) & & \\ Swedish & 15 & Bag-of-features (BOF), LBP & SVM & 99.4 & [35] \\ Flavia & 32 & Digital Morphological Features & Adaboost & 95.42 & [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different feature extraction techniques used by ML models. of applications in the fields of AI and DL. CNNs and their derivatives have produced promising results when it comes to classifying handwritten numbers and recognizing objects and faces. To do this, the CNN expands its reach and improves its capacity to provide distinctive solutions to a variety of problems, including plant categorization in agricultural fields. At the moment, there are a significant number of researchers working on developing the automated analysis of plant images [41; 56]. Researchers in the fields of AI and ML are focused on developing more sophisticated models that can learn new information and extract previously unknown properties. This marks the beginning of work on the very first model trained using deep neural networks. Models such as AlexNet, VGGNet, GoogleNet, and ResNet, as well as their variants, are quite popular and widely used in the field of deep learning. It has achieved tremendous success in a variety of categories as a direct consequence of the enormous advancements made in visual identification and detection [57; 58]. [PERSON] et al. [59] utilized a convolutional neural network and data augmentation techniques such as flipping, scaling, resizing, and rotating to identify 12 plant species based on photos of plant seedlings. [PERSON] et al. [60] created an automated classification system for urban tree species using AlexNet, VGG16, and ResNet50 with the help of RGB optical images. [PERSON] et al. [61] proposed a 26-layer ResNet model for the recognition of plant species from low-resolution images that were captured by mobile phones in a natural scene. Currently, the application of plant classification is more prominent for characterization, taxonomic, and systemic investigations of plants. In addition, several studies have been published in the last decade on plant classification and species identification (Figure 2).