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Advances in Nano Research
  Volume 14, Number 3, March 2023 , pages 225-234
DOI: https://doi.org/10.12989/anr.2023.14.3.225
 


Application of data mining and statistical measurement of agricultural high-quality development
Yan Zhou

 
Abstract
    In this study, we aim to use big data resources and statistical analysis to obtain a reliable instruction to reach high-quality and high yield agricultural yields. In this regard, soil type data, raining and temperature data as well as wheat production in each year are collected for a specific region. Using statistical methodology, the acquired data was cleaned to remove incomplete and defective data. Afterwards, using several classification methods in machine learning we tried to distinguish between different factors and their influence on the final crop yields. Comparing the proposed models' prediction using statistical quantities correlation factor and mean squared error between predicted values of the crop yield and actual values the efficacy of machine learning methods is discussed. The results of the analysis show high accuracy of machine learning methods in the prediction of the crop yields. Moreover, it is indicated that the random forest (RF) classification approach provides best results among other classification methods utilized in this study.
 
Key Words
    crop yield; data mining; machine learning; optimization; statistical analysis
 
Address
Yan Zhou: College of Mathematics and Informatics, South China Agricultural University, 510642 Guangdong, China
 

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