An Intelligent Plant Dissease Detection System for Smart Hydroponic Using Convolutional Neural Network

Musa, Aminu; Hamada, Mohamed; Aliyu, Farouq; Hassan, Mohammed

doi:10.1109/mcsoc51149.2021.00058

Cited by 11 publications

(1 citation statement)

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“…Furthermore, deep learning has made it possible for a convolutional neural network (CNN) to detect and classify different images with higher accuracy. From this view authors of [23] proposed an intelligent disease detection model, using the plant leaves images. This calls the attention of some researchers to investigate the possibility of using CNN in detecting potholes for safer driving.…”

Section: Related Workmentioning

confidence: 99%

A Theoretical Framework Towards Building a Lightweight Model for Pothole Detection using Knowledge Distillation Approach

Musa,

Hamada,

Hassan

2022

SHS Web Conf.

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Despite recent advances in deep learning, the rise of edge devices, and the exponential growth of Internet of Things (IoT) connected devices undermine the performance of deep learning models. It is clear that the future of computing is moving to edge devices. Autonomous vehicles and self-driving cars have leveraged the power of computer vision, especially object detection to navigate through traffic safely. Nevertheless, to be able to drive on all types of roads, these new vehicles have to be equipped with a road anomaly detection system, which strikes the need for small deep learning models of detecting road anomalies that can be deployed on these vehicles for safer driving experience. However, the current deep learning models are not practical on embedded devices due to the heavy resource requirements of the models, as such cannot be deployed on embedded devices. This paper proposes a theoretical approach to building a lightweight model from a cumbersome pothole detection model that is suitable on edge devices using knowledge distillation. It presents the theoretical approach of knowledge distillation, why it is a better technique of model compression compared to the rest. It shows that a cumbersome model can be made lightweight without sacrificing accuracy and with a reduced time complexity and faster training time.

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Section: Related Workmentioning

confidence: 99%