# Leveraging Machine Learning for Weed Management and Crop Enhancement: Vineyard Flora Classification
## 4 Results  
Experiment 1: Testing PyTorch Classification Architectures Using Different Combinations of Hyperparameters  
In the initial experiment, the architectures available in the PyTorch framework were tested using different combinations of hyperparameters. All the executions were trained with a fixed batch size of four, 200 epochs, and 12 distinct parameter combinations in PyTorch. The objective was to identify the most effective model for vineyard flora classification. PyTorch has 20 available architectures; however, some of them have sub-architectures, giving a total of 80 [7]. As the combinations of hyperparameters provide 12 different simulations for each model, there is a total of 960 algorithms to run. However, some models were impossible to run due to the graphics of the computer because it did not have enough memory, especially Vit_1_16, Vit_1_32, Vit_h_14, and InceptionV3. Several models demonstrated high accuracy, with results ranging from 0% to 100%. Figure 3 shows the best accuracies achieved by each model using the small and initial dataset. The values of accuracy presented correspond to the maximum value acquired in each architecture. All the best accuracies achieved by each sub-architecture are shown in Table 1. The table is organized in the following order: name of the model, value of the learning rate, weight decay, number of layers, best validation accuracy, best test accuracy, and finally, the inference time in seconds (sec). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{N.} & \multirow{2}{*}{Models} & \multirow{2}{*}{Learning} & \multirow{2}{*}{Weight} & \multirow{2}{*}{N.\# of} & \multirow{2}{*}{Best Acc} & \multicolumn{2}{c}{Test} & \multirow{2}{*}{Inference} & \multirow{2}{*}{N.} & \multirow{2}{*}{Models} & \multirow{2}{*}{Learning} & \multirow{2}{*}{Weight} & \multirow{2}{*}{N.\# of} & \multirow{2}{*}{Best Acc} & \multirow{2}{*}{Inference} & \multirow{2}{*}{Time (sec)} & \multirow{2}{*}{Time (sec)} & \\  & & **Rate** & **Decay** & **Layers** & **(e)** & **(e)** & & & & & & & & & & \\ \hline
1 & MobileNetV2 & 0.0001 & 0.0001 & Son. & 44.0 & 16.67 & 0.005 & 39 & ShuffleNet, v2.30, 0.5 & 0.0001 & 0 & Lin. & 99.3 & 46.66 & 0.004 \\
2 & MobileNetV2 & 0.0001 & 0.0001 & Lin. & 44.0 & 16.67 & 0.006 & 40 & ShuffleNet, v2.41, 0.001 & 0 & Lin. & 99.3 & 66.67 & 0.034 \\
3 & MobileNetV3 & 0.001 & 0.0001 & Son. & 44.0 & 16.67 & 0.006 & 40 & ShuffleNet, v2.41, 0.001 & 0 & 0.0001 & Lin. & 99.3 & 16.67 & 0.005 \\
4 & MobileNetV3 & 0.001 & 0.0001 & Lin. & 96.0 & 76.67 & 0.005 & 42 & ShuffleNet, v2.42, 0.001 & 0.0001 & Lin. & 99.3 & 66.67 & 0.005 \\
5 & MobileNetV & 0.001 & 0.0001 & Lin. & 96.0 & 76.67 & 0.005 & 42 & ShuffleNet, v2.42, 0.001 & 0.0001 & Lin. & 99.3 & 63.33 & 0.008 \\
6 & Cooglet.net & 0.01 & 0 & Son. & 96.0 & 10.002 & 0.007 & 44 & EfficientNet,v3.01 & 0 & 0 & Lin. & 99.3 & 86.66 & 0.010 \\
7 & V3e,16 & 0.0001 & 0.0001 & Son. & 96.0 & 66.77 & 0.006 & 45 & EfficientNet,v3.02 & 0 & 0.0001 & 56 & 99.3 & 50.00 & 0.011 \\
8 & V3e,32 & 0.00001 & 0.0001 & Lin. & 96.0 & 66.0 & 0.029 & 46 & EfficientNet,v3.03 & 0.001 & 0 & Lin. & 99.3 & 60.00 & 0.012 \\
9 & ResNetV3 & 0.00002, 0.0001 & 0.0001 & Lin. & 80.0 & 13.33 & 0.006 & 47 & ShuffleNet, v4.04 & 0.001 & 0 & Lin. & 99.3 & 73.33 & 0.014 \\
10 & ResNetV3 & 0.0001 & 0.0001 & Son. & 48.0 & 16.67 & 0.013 & 48 & EfficientNet,v3.001 & 0 & 0 & [PERSON]. & 99.3 & 73.33 & 0.017 \\
11 & ResNetV3 & 0.00001 & 0.0001 & Son. & 76.0 & 46.67 & 0.014 & 49 & EfficientNet,v3.001 & 0 & 0 & [PERSON]. & 99.3 & 23.33 & 0.020 \\
12 & ResNetV3 & 0.0001 & 0 & [PERSON]. & 84.0 & 43.33 & 0.002 & 0 & [PERSON]. & 99.3 & 73.33 & 0.014 \\
13 & ResNetV3 & 0.0001 & 0 & Son. & 84.0 & 46.67 & 0.004 & 51 & SuperNetNet,v3.001 & 0 & 0 & [PERSON]. & 99.3 & 23.33 & 0.020 \\
14 & ResNetV3 & 0.0001 & 0 & Son. & 84.0 & 46.67 & 0.004 & 51 & SuperNetNet,v3.001 & 0 & 0 & [PERSON]. & 96.0 & 20.004 \\
15 & ResNetV3 & 0.0001 & 0 & Son. & 93.0 & 70.005 & 52 & SuperNetNet,v3.001 & 0 & 0 & Lin.