Zheng Hai-Ying, Li Yang, Wang Nan, Xiang Yang, Liu Jin-Hang, Zhang Liu-Deng, Huang Lan, Wang Zhong-Yi
College of Information and Electrical Engineering, China Agricultural University, Beijing, China.
Key Laboratory of Agricultural Information Acquisition Technology (Beijing), Ministry of Agriculture, Beijing, China.
PeerJ Comput Sci. 2024 Apr 11;10:e1944. doi: 10.7717/peerj-cs.1944. eCollection 2024.
Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.
电阻抗断层成像（EIT）通过重建电阻抗分布来间接测量玉米穗的生理状态和生长情况。然而，电极平面内的二维（2D）EIT难以全面表征完整玉米穗（包括苞叶、籽粒和穗轴）电导率的空间分布。因此，需要一种有效的三维电导率重建方法。在实际应用中，玉米穗的接触阻抗会出现波动，尤其是在三维空间电导率重建过程中，随着网格数量增加和计算量增大，这种波动会加剧EIT的不适定性和非线性。为应对这些挑战，我们引入了RFNetEIT，这是一种专门为玉米穗三维电阻抗绝对成像量身定制的新型计算框架。该策略将三维电导率重建转化为一个回归过程。首先，通过数据重建模块从测量的边界电压中提取特征图，从而增强不同维度之间的相关性。随后，利用残差网络建立边界电压和电导率三维空间分布的非线性映射模型。通过数值模拟实验、丙烯酸模型实验和玉米穗实验对所提出框架的性能进行评估。我们的实验结果表明，我们的方法在均方根误差（RMSE）、相关系数（CC）、结构相似性指数（SSIM）和反问题求解时间（IPST）方面具有卓越的重建性能。此外，对玉米穗的重建实验表明，该方法能够有效重建三维电导率分布。
