###### Abstract  
Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%. deep learning; energy-aware; low power; knowledge distillation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4](https://creativecommons.org/licenses/py/4)]([https://creativecommons.org/licenses/py/4](https://creativecommons.org/licenses/py/4)). Deep-learning-based plant disease detection models leverage computer vision capability and come up with a model that can diagnose plant diseases by scanning plant leaves. The system is capable of diagnosing a given sample by simply taking the leaf image as input and returning the class of the disease that is affecting the plant on screen to ascertain whether the plant is healthy or not, alongside the name of the diseases that are affecting the plant. However, the context within which the model will be deployed was not considered. The model has a lot of parameters, which results in numerous memory accesses and huge computations making the model resources expensive. The model also uses transfer learning and was controlled by a user via the Internet. From the experimental results, the power consumption of the model increases significantly when Wi-Fi connectivity is turned on. This brings about rapid power dissipation when tested. To overcome these problems, in this paper, an energy-aware intelligent model is proposed where knowledge distillation replaces the transfer learning available in the previous model, making the proposed model, a small compressed version of the previous model that will function completely offline and has low resources requirement. There have been several kinds of research on how to reduce the energy consumption of neural network models. Some of the reported breakthroughs are to perform hardware acceleration, energy management designs, or model compression. Another way of mitigating this energy crisis is to overcome energy shortages in an embedded computing environment with energy harvesting. In this paper, we concentrated on improving our previous work published in MCsoC 2021 [8], by reducing the power consumption of the plant disease detection model from a software perspective. Therefore, energy harvesting and model compression techniques were employed to propose a low-power deep learning model of plant disease detection in smart hydroponics. The rest of the paper is organized as follows: Section 2 presents some background knowledge about Artificial Neural Network and IoT-based hydroponic systems. Section 3 presents previous works that have been found in the literature review. Section 4 presents the proposed system architecture and its components.