## 5 Results and Discussion  
The experimental results indicate that the knowledge distillation technique can be used to build a low-power convolutional neural network model for plant diseases detection, the technique demonstrated how a cumbersome teacher model can be compressed to a lightweight student model with few parameters and low resources utilization (power and storage). Few parameters require less storage space and also indicate that the model will have fewer memory accesses; less memory access guarantees low power consumption. The cumbersome model and low-power model architectures are presented in Figure 6a,b below to highlight their differences in terms of parameters. The proposed low-power model is a shallow neural network with just one hidden layer and two convolutional layers. The total number of parameters in the model is 1.2 M. Whereas the teacher model has about 52 M parameters, the previous model proposed in paper [8] has about 12 M parameters also. This indicates that the proposed low-power model has fewer parameters, which will result in lesser memory accesses and computations, thus reducing power consumption. To effectively measure the performance of the proposed low-power model, we evaluate the performance of the model on the accuracy, power consumption, number of parameters, and training time. The proposed model performance was compared with the cumbersome model and other existing models to prove its effectiveness. To begin with the accuracy of the models, Figures 7-10 presents the accuracy and loss curves of the cumbersome teacher model, as well as the low-power student model. From the results above, the relationship between the size of the model and power consumption was established. We showed that large CNN models give rise to high power consumption from numerous matrix multiplications which leads to huge computations and increases memory accesses. Frequent memory accesses result in increased power consumption. With this notion, we reduced the size of the CNN to a shallow network with fewer memory accesses, and it is capable of detecting these diseases with better accuracy. From Figure 8a,b, it can be noted that the proposed model achieved an overall accuracy of 99.4% on a dataset with 39 different class labels, which surpassed the performance of the cumbersome teacher model presented in Figure 7a,b, as well as the performance of some existing models found in the literature (see Table 2). The overall accuracy we found in existing literature using the Plant Village dataset varied from 85.53% to 99.34%, without consideration of the power consumption or the storage requirements. Hence, the proposed model shows a strong promise of how a low-power deep learning model can be achieved using knowledge distillation techniques for plant disease detection or any other similar detection problems. The proposed low-power model can be deployed effectively on the IoT and embedded devices that are resource-constrained. The proposed low-power student model does not only differ from other models in terms of accuracy but also in several other performance metrics, including power consumption, which is more relevant than other metrics in this paper. The proposed low-power model performances were compared with other models, and the result are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Parameters** & **Accuracy (\%)** & **Epochs** & **Power (W)** & **Time** \\ \hline Teacher model & 52 M & 97.2 & 10 & 9.72 & 2 h 17 min \\ \hline Paper [30] & 12 M & 97 & 25 & 8.9 & 50 min \\ \hline Paper [32] & 7 M & 98.3 & 30 & - & 75 min \\ \hline
**Proposed model** & **1.2 M** & **99.4** & **5** & **6.22** & **7 min** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Model Performance.