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A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan

A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan

Tung-Hsiang Chou, Shih-Chih Chen, Fu-Sheng Tsai
Copyright: © 2022 |Volume: 30 |Issue: 7 |Pages: 18
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668435700|DOI: 10.4018/JGIM.302659
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MLA

Chou, Tung-Hsiang, et al. "A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan." JGIM vol.30, no.7 2022: pp.1-18. http://doi.org/10.4018/JGIM.302659

APA

Chou, T., Chen, S., & Tsai, F. (2022). A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan. Journal of Global Information Management (JGIM), 30(7), 1-18. http://doi.org/10.4018/JGIM.302659

Chicago

Chou, Tung-Hsiang, Shih-Chih Chen, and Fu-Sheng Tsai. "A Cloud-Based Recognition Service for Agriculture During the COVID-19 Period in Taiwan," Journal of Global Information Management (JGIM) 30, no.7: 1-18. http://doi.org/10.4018/JGIM.302659

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Abstract

The great popularity of cloud services, together with the increasingly important aim of providing Internet context-aware services, has spurred interest in developing diverse agriculture applications. This paper presents a cloud-based service built by incrementally integrating state-of-the-art models of deep learning, photography, object recognition and the multi-functionalities of cloud services. This study consists of an object detection phase with a convolutional neural network (CNN) model, which involves enabling simultaneous image data gathered from drones. The experimental results show 97% accurate watermelon recognition. Our results also include a two-model comparison in the cloud-based service, with the main findings demonstrating the feasibility of developing accurate object recognition using a CNN model without the need for additional hardware. Finally, this study adopted a confusion matrix to validate the result with RetinaNet for recognizing images taken on the watermelon farm with an average precision in recognizing watermelon quantity of up to 98.8%.