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Decision Tree and Knowledge Graph Based on Grain Loss Prediction

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Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

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Abstract

China is an agricultural country. Agricultural production is an import part of the Chinese economic system. With the advent of the information age, plenty of data have been produced in a series of links about harvest and after-harvest, such as harvest, processing, transportation, and consumption. With proper use of these data, we can dig out more and more valuable information from the data. In this paper, the relevant algorithm of machine learning is adopted and improved to predict the grain loss after extracting the data of harvesting link. Machine learning is the core of Artificial Intelligence, and its application covers all fields. In this paper, based on the traditional machine learning algorithm—decision tree, the knowledge graph is used to make appropriate improvements to predict the grain-loss after harvest.

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Acknowledgement

China Special Fund for Grain-scientific Research in the Public Interest (201513004), National Natural Science Foundation of China (41671457), Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (16KJA170003).

Author information

Authors and Affiliations

  1. Jiangsu Key Laboratory of Modern Logistics, Jiangsu Key Laboratory of Grain Big Data Mining and Application, College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, Jiangsu, China

    Lishan Zhao & Bo Mao

  2. College of Electrical Engineering and Automation, Jiangsu Maritime Institute, No. 309 Gezhi Road, Nanjing, Jiangsu, China

    Bingchan Li

Authors
  1. Lishan Zhao
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  2. Bingchan Li
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  3. Bo Mao
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Corresponding author

Correspondence to Bo Mao .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

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Cite this paper

Zhao, L., Li, B., Mao, B. (2020). Decision Tree and Knowledge Graph Based on Grain Loss Prediction. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_35

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  • DOI: https://doi.org/10.1007/978-981-15-2810-1_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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