Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Real Time Deployment of MobileNetV3 Model in Edge Computing Devices Using Rgb Color Images for Varietal Classification of Chickpea

Version 1 : Received: 30 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (03:32:49 CEST)

A peer-reviewed article of this Preprint also exists.

Saha, D.; Mangukia, M.P.; Manickavasagan, A. Real-Time Deployment of MobileNetV3 Model in Edge Computing Devices Using RGB Color Images for Varietal Classification of Chickpea. Appl. Sci. 2023, 13, 7804. Saha, D.; Mangukia, M.P.; Manickavasagan, A. Real-Time Deployment of MobileNetV3 Model in Edge Computing Devices Using RGB Color Images for Varietal Classification of Chickpea. Appl. Sci. 2023, 13, 7804.

Abstract

Chickpea is one of the most widely consumed pulses globally because of its high protein content. The morphological features of chickpea seed, such as colour, texture, are observable and play a major role in classifying different chickpea varieties. This process is often carried out by human experts, and is time-consuming, inaccurate, and expensive. The objective of the study was to design an automated chickpea classifier using an RGB colour image-based model by considering the morphological features of chickpea seed. As part of the data acquisition process, five hundred and fifty images were collected per variety for four varieties of chickpea (CDC-Alma, CDC-Consul, CDC-Cory, and CDC-Orion) using an industrial RGB camera and a mobile phone camera. Three CNN-based models such as NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 were evaluated using a transfer learning-based approach. The classification accuracy was 97%, 99%, and 98% for NasNet-A (mobile), MobileNetV3 (small), and EfficientNetB0 models, respectively. The MobileNetV3 model was used for further deployment on an Android mobile and Raspberry Pi 4 devices based on its higher accuracy and light-weight architecture. The classification accuracy for the four chickpea varieties was 100% while the MobileNetV3 model was deployed on both Android mobile and Raspberry Pi 4 platforms.

Keywords

chickpea; convolutional neural network; transfer learning; classification

Subject

Engineering, Bioengineering

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