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Deep learning in the information service system of agricultural Internet of Things for innovation enterprise

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Abstract

To discuss the application of Internet of Things (IoT) in the agriculture, an agricultural product price prediction model is constructed based on the improved Elman neural network (ENN) of deep learning. Simulation experiment of pest prediction is carried out based on MATLAB, and then the agricultural IoT information service system is combined with the improved ENN prediction model to predict and analyze agricultural product prices. The results show that the agricultural product price prediction model based on the improved ENN has high accuracy, which reaches 0.9241. Through this agricultural product price prediction model, the agricultural IoT information service system can predict the price trend of agricultural products better, and it can also predict the shortcomings in the production and sales of agricultural products, so that corresponding management and control measures can be realized. This work can provide an important reference for the application of deep learning methods in agricultural IoT.

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Funding

Project of Zhejiang Higher Education Association: Research on talent Training Mode of “Entrepreneurship and Innovation Education “to Promote New Economic Development (KT2020097). Industry and Education Integration project of the Ministry of Education: “Internet + ” Entrepreneurship Foundation Course Teaching Ability Improvement (201902128018).

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Authors and Affiliations

  1. College of Entrepreneurship, Jiaxing University, Jiaxing, China

    Qingquan Liu & Zuo Jun

  2. Heilongjiang Provincial Academy of Social Sciences, Harbin, China

    Yingli Wu

  3. Economics College, Jiaxing University, Jiaxing, China

    Xin Li

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  1. Qingquan Liu
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  4. Xin Li
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Correspondence to Xin Li.

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Liu, Q., Wu, Y., Jun, Z. et al. Deep learning in the information service system of agricultural Internet of Things for innovation enterprise. J Supercomput 78, 5010–5028 (2022). https://doi.org/10.1007/s11227-021-04070-2

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