基于短时傅里叶光谱与数据融合的土壤成分含量预测

Prediction of soil composition content based on short-time Fourier spectroscopy and data fusion

土壤肥力是衡量土壤质量的重要指标.为了评估土壤质量和提高作物产量,迫切需要找到快速预测土壤成分的途径.首先提出Inception层短时傅里叶变换卷积神经网络(inception short-time Fourier transform convolutional neural network,简称I-STFT-CNN)单一光谱模型,然后提出2个融合光谱模型II-STFT-CNN(indirect inception short-time Fourier transform convolutional neural network)和CI-STFT-CNN(cascade inception short-time Fourier transform convolutional neural network),最后对这些光谱模型的性能参数进行对比.研究结果表明:相对于SVR(support vector regression),PLSR(partial least squares regression)和STFT-CNN(short-time Fourier transform convolutional neural network)模型,该文提出的单一光谱I-STFT-CNN模型具有更高的预测精度;融合光谱模型的预测精度优于单一光谱模型;两个融合模型中,级联融合CI-STFT-CNN模型的性能优于通道融合II-STFT-CNN模型.因此,3种模型中,CI-STFT-CNN模型的预测精度最高.

Soil fertility serves as a crucial indicator of soil quality.To enhance crop yield and evaluate soil quality,it is imperative to rapidly forecast soil makeup by discovering new approaches.Firstly,a single spectral model of inception layer short-time Fourier transform convolutional neural network(I-STFT-CNN)was proposed.Then,two fusion spectral models II-STFT-CNN(indirect inception short-time Fourier transform convolutional neural network)and CI-STFT-CNN(cascade inception short-time Fourier transform convolutional neural network)were proposed.Finally,the performance parameters of these spectral models were compared.The results showed that compared with SVR(support vector regression),PLSR(partial least squares regression)and STFT-CNN(short-time Fourier transform convolutional neural network)model,the single spectral I-STFT-CNN model proposed in this paper had higher prediction accuracy.The prediction accurary of fusion spectral model was better than that of single spectral model.Among the two fusion models,cascade fusion CI-STFT-CNN model had better performance than channel fusion II-STFT-CNN model.Therefore,among the three models,the CI-STFT-CNN model had the highest prediction accuracy.