# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 2 Related Work
### Neural Network Architectures
Wide ResNet, on the other hand, increases the width of residual blocks, leading to a wider network with more feature channels, enhancing the capacity for capturing complex patterns [15]. While ResNet focuses on depth, ResNeXT emphasizes multi-path aggregation and Wide ResNet emphasizes increased width, providing different strategies for improving the expressiveness and learning capabilities of deep neural networks [14; 15]. ViT (Vision Transformer), Swin Transformer, and MaxVit are advanced DL architectures for computer vision tasks. ViT pioneered the transformer architecture for vision tasks, using self-attention mechanisms to process image patches directly, but it struggles with handling large images due to quadratic complexity. It integrates self-attention layers and feed-forward networks to process patch embeddings [16]. The Swin Transformer introduces hierarchical transformers, organizing layers into stages and allowing cross-stage information flow, enabling efficient parallel processing of image patches [17]. MaxVit addresses ViT's limitations by introducing a novel variant with a maximal pooling strategy, reducing computational complexity while maintaining strong performance on large images. Each approach provides unique solutions to leverage transformers for image understanding, catering to different computational and task-specific requirements. The architecture uses multi-axis attention and convolution, processing input images with a convolutional stem. It employs mobile inverted bottleneck convolution blocks for computational efficiency and a grid structure for object recognition and pattern analysis [18]. Both MobileNet and ShuffleNet V2 architectures focus on computational efficiency for mobile and edge devices and aim to reduce computational cost and model size while maintaining accuracy. MobileNet uses depth-wise separable convolutions and introduces width and depth multipliers, allowing adjustments between model size and accuracy [10]. ShuffleNet V2 combines depth-wise convolutions with compact \(1\times 1\) convolutions and introduces channel shuffling and pointwise group convolutions, enabling efficient information exchange across channels and reducing computational complexity further [19]. This process starts with the \"Channel Split\" procedure, where the input feature map's channels are divided into two branches of equal size. One branch remains unaltered, while the other undergoes three convolution layers. The outputs of these branches are then merged using the concatenation operation, \"Concat\", after convolution, ensuring an equal number of channels in the combined output. Subsequently, the \"channel shuffle\" mechanism is applied, enabling effective communication and interaction between these branches [10; 19]. EfficientNet, EfficientNetV2, and MNASNet are designed with a focus on efficient scaling. EfficientNet optimizes model size, depth, and width simultaneously using compound scaling, providing a balanced trade-off between accuracy and computational cost [20]. EfficientNetV2, an evolution of it, refines the design principles, introducing improved factorized convolutions for enhanced efficiency and accuracy [11]. MNASNet employs an automated neural architecture search (NAS) to discover efficient model architectures and customize networks to specific hardware constraints and use cases [20]. While EfficientNet and EfficientNetV2 focus on efficient scaling, MNASNet emphasizes automated search methods, offering distinct approaches to achieving efficiency and accuracy in neural network design.