## 6 Conclusions  
In this paper, we presented a low-power convolutional neural network model for plant disease detection in smart hydroponics using the knowledge distillation technique. Power consumption is a critical resource in an embedded system environment such as smart hydroponics. For an effective solution to plant disease diagnosis in smart hydroponics, deep-learning-based models have been proposed in the past. Unfortunately, those models suffer from high power consumption, high inference times, and consumed large space on disks. Therefore, this paper presents an energy-efficient, low-power deep learning model for plant disease detection in smart hydroponics. The model achieved a state-of-the-art accuracy of 99.4%, higher than that of existing models, with a low average power consumption of 6 w. The system was also complemented with an energy-harvesting technique to augment the recharging of the edge devices when their battery is low. The proposed model was aimed at improving the accuracy and power consumption of the existing models by reducing the number of parameters in the model, minimizing computations, and providing a faster model that is more accurate and scalable.  
The proposed low-power model obtained an increase in accuracy of 2.4% compared to our previous CNN model despite its light structure. The model also records a maximum power consumption and energy usage of 6.22 w and 170.54 J, respectively. This shows a slight decrease in power consumption when compared to our previous model [8] with 8.9 w. However, the energy usage of the two models differs significantly. The CNN model has higher energy usage compared to the proposed KD model, which indicated that the proposed model is indeed energy-efficient. In terms of speed, the latency of the two models were highlighted in table 3, which also shows that the proposed KD model has a lowest latency of 29.17 s. During training as well, KD model was only trained for 5 epochs, which last for 7 min. In comparison, the CNN model was trained for 10 epochs, which lasted more than 50 min. In terms of inference, when deployed on raspberry pi, the proposed model took an average of 50 s per image while utilizing 6.22 w power; consequently, the teacher model spent more than 80 s per image with a power consumption of 6.25 w on the same device. Conclusively, the proposed low-power model is more accurate, energy-efficient, lighter, faster, and more suitable for smart hydroponics environment compared to existing models of plant disease detection. This paper is limited to the deep-learning-based method of plant diagnosis, which uses plant leaves as a dataset. The model cannot detect other conditions apart from the ones it was trained on and cannot give solutions to the detected diseases affecting the plant.  
Conceptualization, all authors; methodology, all authors; Experiments, A.M. and F.A.; coding and validation, A.M. and M.H. ([PERSON]); Investigation, A.M. and F.A.; resources, F.A. and M.H. ([PERSON]); writing-original draft preparation, A.M.; writing-review and editing, M.H. ([PERSON]) and A.M.; supervision, M.H. ([PERSON]) and M.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Maximum Power (W)** & **Energy (J)** & **Latency (S)** \\ \hline CNN Model [30] & 6.25 & 339.47 & 57.18 \\ \hline Proposed model & 6.22 & 170.54 & 29.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of power consumption.