Rapid detection of corn moisture content(MC) during maturity is of great significance for field cultivation, mechanical harvesting, storage, and transportation management. However, cumbersome operation, time-consuming and labor-intensive operation were the bottleneck in the traditional drying process and dielectric parameter method. Thus, to overcome the above problems, a rapid detection method for corn MC based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with temporal convolutional network-bidirectional gated recurrent unit (TCN-BiGRU) model. First, based on the 405 groups of NIR spectral data of corn seeds, the crested Porcupine Optimizer (CPO) algorithm was used to optimize ICEEMDAN to reduce the noise of the original spectral data. Then the Chaotic-Cuckoo Search (CCS) algorithm was applied to extract 203 characteristic wavenumbers from the original spectrum, which were input into the constructed TCN-BiGRU network model to realize corn MC detection. Finally, the CPO-ICEEMDAN-CCS-TCN-BiGRU corn MC classification detection model was constructed. The result showed that the model accuracy was 97.54 %, which was 9.22 %, 5.58 %, 2.34 %, 4.74 %, and 5.94 % higher than those of convolutional neural networks (CNN), long short-term memory networks (LSTM), temporal convolutional network (TCN), partial least squares (PLS), and support vector machine (SVM) models, respectively. The research results can provide a reliable basis for improving corn yield, quality and economic benefits.

中文翻译：

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基于改进ICEEMDAN算法结合TCN-BiGRU模型的玉米含水率快速检测


玉米成熟期含水率 （MC） 的快速检测对大田耕作、机械收获、储存和运输管理具有重要意义。然而，操作繁琐、耗时和劳动密集型操作是传统干燥工艺和介电参数法的瓶颈。因此，为了克服上述问题，一种基于改进的自适应噪声完全集成经验模态分解 （ICEEMDAN） 结合时间卷积网络双向门控循环单元 （TCN-BiGRU） 模型的玉米 MC 快速检测方法。首先，基于405组玉米种子近红外光谱数据，采用冠状箭猪优化器（CPO）算法对ICEEMDAN进行优化，以降低原始光谱数据的噪声;然后，应用 Chaotic-Cuckoo Search （CCS） 算法从原始光谱中提取 203 个特征波数，并将其输入到构建的 TCN-BiGRU 网络模型中，实现玉米 MC 检测。最后，构建了CPO-ICEEMDAN-CCS-TCN-BiGRU玉米MC分类检测模型。结果表明，模型准确率为 97.54 %，比卷积神经网络 （CNN）、长短期记忆网络 （LSTM）、时间卷积网络 （TCN）、偏最小二乘法 （PLS） 和支持向量机 （SVM） 模型分别高 9.22 %、5.58 %、2.34 %、4.74 % 和 5.94 %。研究结果可为提高玉米产量、品质和经济效益提供可靠依据。