彭旭, 饶元, 乔焰. 基于宽度卷积神经网络的异常农情数据检测方法[J]. 华南农业大学学报, 2022, 43(2): 113-121. DOI: 10.7671/j.issn.1001-411X.202103050
    引用本文: 彭旭, 饶元, 乔焰. 基于宽度卷积神经网络的异常农情数据检测方法[J]. 华南农业大学学报, 2022, 43(2): 113-121. DOI: 10.7671/j.issn.1001-411X.202103050
    PENG Xu, RAO Yuan, QIAO Yan. Detection method of abnormal agricultural data based on broad convolution neural network[J]. Journal of South China Agricultural University, 2022, 43(2): 113-121. DOI: 10.7671/j.issn.1001-411X.202103050
    Citation: PENG Xu, RAO Yuan, QIAO Yan. Detection method of abnormal agricultural data based on broad convolution neural network[J]. Journal of South China Agricultural University, 2022, 43(2): 113-121. DOI: 10.7671/j.issn.1001-411X.202103050

    基于宽度卷积神经网络的异常农情数据检测方法

    Detection method of abnormal agricultural data based on broad convolution neural network

    • 摘要:
      目的  为准确有效地检测农业物联网的感知数据异常,提出了基于宽度卷积神经网络的异常农情数据检测方法,为实现农业物联网数据高质量感知提供参考。
      方法  首先将标准化后的农情数据编码为极坐标表示,通过滑动窗口机制划分子集,接着将每个子集数据重构为矩阵,最后设计并训练宽度卷积神经网络模型用于异常检测,采用养殖场环境监测数据进行试验。
      结果  构建的滑动窗口机制可提升异常数据检测能力,缩短检测时间。所设计的宽度卷积神经网络对空气温湿度、土壤温湿度等数据中所存在的异常检测准确率均超过97.5%,优于SVM、RF和CNN模型1.69%、2.76%和3.05%;F1值均在0.985以上,优于SVM、RF和CNN模型0.0093、0.0149和0.0163;且在处理波动性较大的空气、土壤温湿度数据时性能优势更为明显,准确率和F1值分别提高了3.61%~5.98%和0.0188~0.0310。此外,该方法模型检测耗时较短,仅为传统CNN模型的1/6~1/7,并且比SVM和RF模型使用更少的超参数。
      结论  所建立的数据编码、子集划分和重构方法与宽度卷积神经网络模型对异常农情数据有较好的检测效果。

       

      Abstract:
      Objective  In order to accurately and effectively detect data anomalies from agricultural internet of things, one detection method of abnormal agricultural data based on broad convolution neural network (BCNN) was proposed for providing a reference for achieving high-quality data collection in agricultural internet of things.
      Method  Firstly, the standardized agricultural data were encoded as polar coordinates, and then they were divided into subsets by sliding window mechanism. Subsequently, the data were reconstructed as matrix format. Finally, the BCNN was designed and trained for conducting anomaly detection. The experiment was conducted using the data monitored in the culturing farm environment.
      Result  The sliding window mechanism could improve the detection ability and reduce the time consumption. The accuracy and F1 score of the designed BCNN in the datasets of air temperature and humidity, soil temperature and humidity were more than 97.5% and 0.985 respectively, which on average outperformed SVM, RF and CNN with the increase of 1.69%, 2.76%, 3.05% and 0.009 3, 0.0149, 0.0163, respectively. In particular, while handling the air and soil temperature and humidity data with high fluctuation , the gain in accuracy and F1 score ranged 3.61%–5.98% and 0.018 8–0.031 0, respectively. In addition, the proposed BCNN model has less time consumption of anomaly detection, only 1/6 to 1/7 of classical CNN model, and as well as with less hyperparameter.
      Conclusion  The proposed data preprocessing (data coding, subset partition and reconstruction) method and BCNN model exhibit better performance on abnormal agricultural data.

       

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