Abstract
In this study, a plurality of camera sensors distributed in the agricultural land was integrated into the Raspberry Pi, and photos were taken to observe whether the foliage of the crop was harmful or not. The image data were transmitted to the Alexnet, VGG-16 and VGG-19 convolutional nerves through deep learning methods. The network architecture extracts image features to detect the presence of pests and identifies the types of pests. Compared by the classification accuracy, training model and prediction time with a classifier based on a neural network, and a Support Vector Machine, the identified pest results will be immediately displayed on the farming management app as a timely epidemic prevention management of the farming.
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Acknowledgments
This research is supported by the Ministry of Science and Technology, Taiwan, R.O.C. under grant nos. MOST 107-2321-B-067F-001- and MOST 106-2119-M-309-002-MY2, which is also financially partially supported by the “Intelligent Recognition Industry Service Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
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Chen, CJ., Wu, JS., Chang, CY., Huang, YM. (2020). Agricultural Pests Damage Detection Using Deep Learning. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_53
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DOI: https://doi.org/10.1007/978-3-030-29029-0_53
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