# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 3 Results
### Online Validation of the Models  
The segmented images from the grapevine plants that have been produced by the Mobile-UNet segmentation algorithm proceed to the fine-tuned DL models in order for them to be characterised as either healthy or infected. As was already mentioned, the validation of the models was performed in parts of four rows from the Chatzivariti winery (two from each of the different variety fields), where there were visible symptoms of esca in the grapevine plants, as was proposed by the plant pathologists. The image acquisition in this study was performed with the RGB camera that was mounted on the RPi platform, facing directly on the grapevine plants. The whole system  
Figure 11: Example of Mobile-UNet’s performance on the online segmentation of the grapevine from its background surrounding. **Left**: the original image, as was captured by the RGB camera; **Middle**: the ground truth mask that was used for the evaluation; and **Right**: the segmentation mask, as it was produced by the Mobile-UNet algorithm. Figure 12: Application of the segmented mask by the Mobile-UNet algorithm on the photos that were captured during the field survey. was recording for approximately 50 s, covering a row of almost 65 m, while there were two runs per row in order to deploy both of the DL models that were examined (MobileNet V2 and EfficientNet B0). The average accuracy results, as well as the models' inference time for the validation phase of the whole system, is shown in Table 6.  
Table 6 illustrates a discernible drop in accuracy across the board for both models when they were validated on the Chatzivariti winery. These results demonstrate the high effectiveness of both models in accurately classifying the incoming images. MobileNet, with its lightweight architecture and efficient design, has proven to be highly adept at recognising and categorising diseases in field conditions. Its remarkable accuracy of 0.941 indicates that it can reliably make correct predictions in a wide range of scenarios. On the other hand, EfficientNet B0, also had a very high performance, with an accuracy of 0.924. It should be noted that the evaluation metrics are calculated as if it was a binary classification problem with only the healthy and Esca class; this is due to too few instances in most classes. The higher F1 score that is reported is most likely due to the high imbalance between the different classes, as can be also validated by Figure 13.  
As mentioned previously, the data used for the validation stage included healthy and esca-infected plants. Classes also exhibit imbalances as the real-world data had far more healthy plants than infected, as would be expected. Nevertheless, the algorithms performed excellently in each case, with only a small drop of around 5% in validation accuracy. That is further shown in the classification of each class (Figure 13), where the healthy status was correctly predicted, while the few misclassified Esca observations, especially in the case of MobileNet v2, were mainly attributed to the Eirobyes v. class. This may be a result of similarities in the leaf textures caused by the two different diseases. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **EfficientNet B0** & **MobileNet V2** \\ \hline Validation accuracy & 0.924 & 0.941 \\ F1 score & 0.949 & 0.961 \\ Inference Time [ms] & 390 & 330 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Validation accuracy results and inference time of the proposed deep learning models with online deployment on the grapevine plants from [PERSON]’s winery. Figure 13: Confusion Matrix of EfficientNet B0 (**left**) and MobileNet V2 (**right**) during the online validation. The total inference time for both models was less than 400 ms (Table 6). Specifically, MobileNet V2 had a slightly faster inference, in comparison with the EfficientNet B0.