Article: Plant leaf disease detection using deep learning on mobile devices Journal: International Journal of Computational Vision and Robotics (IJCVR) 2022 Vol.12 No.2 pp.156 - 176 Abstract: Conventional plant disease detection by human experts is subjective, sensitive to human errors, and requiring specialised training. Computer vision algorithms powered by deep convolutional neural network (DCNN) models have the ability to improve the plant leaf disease detection. In this paper, we investigate the practicability of deploying a DCNN-based solution on mobile/embedded devices in terms of accuracy and performance. We exploit MobileNetV2, one of the DCNN models commonly used with embedded devices, and another heavy DCNN model which is not designed for embedded devices, i.e., AlexNet to assess the performance loss compared to the accuracy gain. Our results using plant village benchmark dataset show that the achieved accuracy is 96.54% and 97.87% for MobileNetV2 and AlexNet, respectively. For the inference performance, the best performance is mostly achieved when the embedded GPU is utilised. It takes 26.3 and 27.5 milliseconds on the average for MobileNetV2 and AlexNet, respectively on a professional class mobile device and 155.07 and 80.67 milliseconds on the average for MobileNetV2 and AlexNet, respectively on an average class mobile device. We conclude that the advanced computational power of current mobile devices enables heavy-weighted DCNN models to be efficiently deployed and hence achieving high accuracy without scarifying the performance. Inderscience Publishers - linking academia, business and industry through research

Title: Plant leaf disease detection using deep learning on mobile devices

Authors: Shaheera A. Rashwan; Marwa K. Elteir

Addresses: Informatics Research Institute, City of Scientific Research and Technological Applications, Alexandria, Egypt ' Informatics Research Institute, City of Scientific Research and Technological Applications, Alexandria, Egypt

Abstract: Conventional plant disease detection by human experts is subjective, sensitive to human errors, and requiring specialised training. Computer vision algorithms powered by deep convolutional neural network (DCNN) models have the ability to improve the plant leaf disease detection. In this paper, we investigate the practicability of deploying a DCNN-based solution on mobile/embedded devices in terms of accuracy and performance. We exploit MobileNetV2, one of the DCNN models commonly used with embedded devices, and another heavy DCNN model which is not designed for embedded devices, i.e., AlexNet to assess the performance loss compared to the accuracy gain. Our results using plant village benchmark dataset show that the achieved accuracy is 96.54% and 97.87% for MobileNetV2 and AlexNet, respectively. For the inference performance, the best performance is mostly achieved when the embedded GPU is utilised. It takes 26.3 and 27.5 milliseconds on the average for MobileNetV2 and AlexNet, respectively on a professional class mobile device and 155.07 and 80.67 milliseconds on the average for MobileNetV2 and AlexNet, respectively on an average class mobile device. We conclude that the advanced computational power of current mobile devices enables heavy-weighted DCNN models to be efficiently deployed and hence achieving high accuracy without scarifying the performance.

Keywords: plant leaf disease detection; convolutional neural network; mobile devices; embedded GPUs; TensorFlow; MobileNetV2; AlexNet.

DOI: 10.1504/IJCVR.2022.121151

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.2, pp.156 - 176

Received: 22 Jul 2020
Accepted: 17 Jan 2021
Published online: 28 Feb 2022 *

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Title: Plant leaf disease detection using deep learning on mobile devices

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