# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 2 Related Work  
### Neural Network Architectures  
CNN models improve image analysis accuracy and efficiency. PyTorch, an open-source platform, offers flexibility, intuitiveness, and dynamic computing graphs for building and training NNs. With twenty different architectures, and multiple sub-architectures, it offers distinct models for various image classification tasks [7]. AlexNet and Visual Geometry Group (VGG) are both influential convolutional neural network architectures for image classification. AlexNet was one of the first deep models to gain widespread attention, employing five convolutional layers with max pooling and three fully connected layers. VGG is characterized by its simplicity, consisting of deeper convolutional layers (up to 19 layers) with small 3 \(\times\) 3 kernels and max pooling, followed by fully connected layers. While AlexNet emphasizes the use of Rectified Linear Unit (ReLU) activations and dropout for regularization, VGG focuses on exploring the impact of increasing network depth, paving the way for subsequent deep architectures [8]. ConvNeXt employs group convolutions and path aggregation to efficiently learn features by dividing input channels into groups. It balances computational efficiency and representational strength using cardinality, offering various depth and capacity options [9]. SqueezeNet focuses on compactness, utilizing 1 \(\times\) 1 convolutions and skip connections to reduce parameters and address gradient flow issues. It strikes a balance between model size and accuracy, making it ideal for devices with limited resources [10]. RegNet adopts compound scaling and adaptive rules, allowing uniform adjustment of network depth, width, and resolution, providing flexibility in customizing model capacity and computational resources [11]. GoogLeNet introduced inception modules employing parallel convolutional processes with filter sizes of \(1\times 1\), \(3\times 3\), and \(5\times 5\), along with max pooling, effectively capturing local and global information. Also, \(1\times 1\) convolutional layers are used for dimensionality reduction, optimizing computational resources. Auxiliary classifiers, with fully connected and \(1\times 1\) convolutional layers, address the vanishing gradient problem, promoting distinctive feature learning. GoogLeNet employs global average pooling instead of fully connected layers, minimizing overfitting. With 22 layers and more filters per layer, it showcases advanced performance in deep learning applications [9]. InceptionV3 is an evolution of GoogLeNet. It is a newer version with deeper architecture and fewer parameters, making it more efficient and accurate. It uses innovative inception modules and factorized convolutions, allowing it to capture features at various scales [12]. DenseNet and Residual Network (ResNet) are both deep neural network architectures. DenseNet achieves dense connectivity by connecting each layer to every other layer in a feed-forward structure. This promotes feature reuse and addresses the vanishing gradient problem. It emphasizes dense connections between layers, where each layer receives direct input from all preceding layers, promoting feature reuse and gradient flow. DenseNet's dense connections lead to parameter efficiency and feature reuse, enabling better gradient flow [10]. ResNet also utilizes a similar concept of residual connections, ensuring smooth gradient flow during training, allowing for the easy training of very deep networks by learning residual functions. ResNet's residual connections help in mitigating vanishing gradient problems, enabling the training of very deep networks. Both approaches have significantly impacted deep learning but employ different strategies for information propagation within the network [13]. ResNeXT and Wide ResNet are variants of the original ResNet architecture. ResNet introduces residual blocks. ResNeXt enhances ResNet by using a split-transform-merge strategy, aggregating information through parallel paths, and improving representational capacity [14].