Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

Yong Li, Hebing Liu, Jialing Wei, Xinming Ma, Guang Zheng and Lei Xi ()
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Yong Li: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Hebing Liu: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Jialing Wei: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Xinming Ma: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Guang Zheng: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Lei Xi: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2023, vol. 13, issue 3, 1-16

Abstract: The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Keywords: mobile edge computing; convolutional neural network; deep reinforcement learning; wheat growth stages detection; dynamic migration algorithm (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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