# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 5 Discussion of Results  
The following findings and challenges can be determined by the results of the actual tests. The first experiment effectively found both the ideal hyperparameter combination and the top-performing simulated models. A default weight decay setting was utilized in 56 instances, while a variable weight decay was selected in 20 cases. Sequential fully connected layers outperformed linear configurations in 42 instances out of 76 tests. The selection of hyperparameters such as learning rate, weight decay, and layer configuration significantly influences model performance. Lower learning rates, such as 0.0001, contribute to stable convergence and prevent the overshooting of optimal solutions. Default weight decay (with a value of zero) served as effective regularization, enhancing model generalization. Sequential fully connected layers proved efficient in capturing complex patterns and relationships in the data [34; 35; 36]. According to the results, the three architectures were chosen as the better ones and used in the following experiments. The study identified MaxVit, ShuffleNet, and EfficientNet as superior models. Despite MaxVit only reaching a maximum accuracy of 96%, this architecture was chosen because it achieved this accuracy value during several epochs and repeated the process in several tests with different combinations of hyperparameters. On the other hand, the other architectures that achieved an accuracy of 99.3%, such as ConvNeXt_Tiny, Convnext_small, Swin_v2_t, DenseNet121, DenseNet169, MNASNet0_75, and MNASNet1_0, were not considered the best because they reached their maximum in a lower number of epochs than MaxVit and fewer tests with different hyperparameter configurations. The three architectures achieved a high accuracy and good results over the 200 epochs, and they were the ones that achieved this accuracy more times over the 12 combinations. These models distinguished themselves through innovative, resource-efficient designs, leveraging available resources optimally and striking a balance between depth and complexity. The fine-tuning of hyperparameters and high-quality, meticulously labeled data were crucial contributors to their success [18; 19]. Figure 7: Model performance of the top twelve models after the testing phase. According to the inference times, some images take only a few seconds (e.g., 0.001 s), while others might take longer (e.g., 0.055 s). Inference times that differ plenty may indicate that some images are more difficult or time-consuming for the model to process. Despite this, all the inference timings were less than one second. However, the sub-architectures' average time inside the same architectures was consistent, indicating a stable performance. In the second experiment, the ShuffleNet_v2_x0_5 model exhibited the lowest accuracy at 84%, followed by EfficientNet_B2 and EfficientNet_B6 with an accuracy of 92%. The rest of the models achieved a maximum accuracy of 96%. With only 25 epochs, these models might not have had enough time to converge to an optimal solution. It is recommended to extend the training duration by increasing the number of epochs, allowing these models a better opportunity to converge and extract valuable insights from the dataset [42]. For the third experiment, it is possible to conclude that with a larger dataset, these three models achieved very good accuracy as well. However, it reached accuracy values slightly lower than the original dataset that was developed for this purpose. These disparities can introduce different complexities for the models, potentially posing a slightly greater challenge in achieving the same level of accuracy [43]. Furthermore, it is possible to distinguish EfficientNetB1 from the other models. The larger dataset may have unique characteristics and variations compared to the smaller dataset, introducing complexities for models. The larger dataset offers a more diverse array of examples, making it more challenging for models to overfit.