In the study of brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs), how to improve the classification accuracies of BCIs has always been the focus of researchers. Canonical correlation analysis (CCA) is widely used in BCI systems of SSVEPs because of its rapidity and scalability. However, the classical CCA algorithm always encounters the difficulty of low accuracy in a short time. For targetless stimuli, this paper proposes a fusion algorithm (CCA-CWT-SVM) that is combined with CCA, a continuous wavelet transform, and a support vector machine (SVM) to improve the low classification accuracies when a single feature extraction method is used. This fusion algorithm achieves high accuracies and information transfer rates (ITRs) in the SSVEP paradigm with few targets. Through the study of 400 groups of experimental data from 10 subjects, the results show that CCA-CWT-SVM has a classification accuracy of 91.76% within 2 s and an ITR of 48.92 bits/min, which are 10.88% and 13.18 bits/min higher than those of the standard CCA. Compared with a mainstream EEG decoding algorithm, filter bank canonical correlation analysis (FBCCA), the classification accuracy and ITR of the CCA-CWT-SVM algorithm also improved (4.45% and 5.69 bit/min, respectively). Using a dataset from Tsinghua University (THU), we also showed that the fusion algorithm is better than the classical algorithms. The CCA-CWT-SVM algorithm obtained an 89.1% accuracy and a 39.91 bit/min ITR in a time window of 2 s. The results were significantly improved compared with those of CCA and the FBCCA (CCA: 79.44% and 28.23 bits/min, FBCCA: 84.03% and 33.4 bits/min). Hence, this work provides an experimental basis for designing an SSVEP-based BCI system with a high task classification accuracy in some crucial biomedical applications.

As one of the world’s most crucial crops, the potato is an essential source of nutrition for human activities. However, several diseases pose a severe threat to the yield and quality of potatoes. Timely and accurate detection and identification of potato diseases are of great importance. Hyperspectral imaging has emerged as an essential tool that provides rich spectral and spatial distribution information and has been widely used in potato disease detection and identification. Nevertheless, the accuracy of prediction is often low when processing hyperspectral data using a one-dimensional convolutional neural network (1D-CNN). Additionally, conventional three-dimensional convolutional neural networks (3D-CNN) often require high hardware consumption while processing hyperspectral data. In this paper, we propose an Atrous-CNN network structure that fuses multiple dimensions to address these problems. The proposed structure combines the spectral information extracted by 1D-CNN, the spatial information extracted by 2D-CNN, and the spatial spectrum information extracted by 3D-CNN. To enhance the perceptual field of the convolution kernel and reduce the loss of hyperspectral data, null convolution is utilized in 1D-CNN and 2D-CNN to extract data features. We tested the proposed structure on three real-world potato diseases and achieved recognition accuracy of up to 0.9987. The algorithm presented in this paper effectively extracts hyperspectral data feature information using three different dimensional CNNs, leading to higher recognition accuracy and reduced hardware consumption. Therefore, it is feasible to use the 1D-CNN network and hyperspectral image technology for potato plant disease identification.

Traditional crop classification methods have three critical limitations: (1) dependency on labor-intensive field surveys with limited spatial coverage, (2) susceptibility to human subjectivity during manual data collection, and (3) the inability to capture fine-grained spectral variations due to the lack of multispectral analysis. This research introduces an enhanced crop classification and identification model based on a residual ResNet network. This model leverages multispectral remote sensing images from unmanned aerial vehicles (UAVs) to accurately classify complex crop planting structures. The research focuses on four typical crops: sunflower, corn, beet, and pepper. By acquiring and preprocessing multispectral remote sensing image data, an improved ResNet50 model integrating the ACmix self-attention module and a coordinate attention mechanism is developed to enhance the classification and recognition accuracy of these crops. Experimental results demonstrate that the improved model achieves a classification accuracy of 97.8% on multispectral images, outperforming both RGB images and traditional methods. This research highlights the potential of combining UAV multispectral remote sensing technology with deep learning for precise crop classification, offering valuable technical support for precision agriculture management.

Aiming at the feature extraction of left- and right-hand movement imagination EEG signals, this paper proposes a multichannel correlation analysis method and employs the Directed Transfer Function (DTF) to identify the connectivity between different channels of EEG signals, construct a brain network, and extract the characteristics of the network information flow. Since the network information flow identified by DTF can also reflect indirect connectivity of the EEG signal networks, the newly extracted DTF features are incorporated into the traditional AR model parameter features and extend the scope of feature sets. Classifications are carried out through the Support Vector Machine (SVM). The classification results show the enlarged feature set can significantly improve the classification accuracy of the left- and right-hand motor imagery EEG signals compared to the traditional AR feature set. Finally, the EEG signals of 2 channels, 10 channels, and 32 channels were selected for comparing their different effects of classifications. The classification results showed that the multichannel analysis method was more effective. Compared with the parameter features of the traditional AR model, the network information flow features extracted by the DTF method also achieve a higher classification effect, which verifies the effectiveness of the multichannel correlation analysis method.

Aiming at the shortcomings of traditional methods for detecting the content of Alkaline Hydrolysis Nitrogen (AHN) and pH value in soil, such as time-consuming and labor-consuming, this paper proposes a rapid quantitative inversion method based on hyperspectral analysis of AHN content and pH value. This method uses db4 discrete wavelet denoising (DWD) and wavelet denoising normalization (DWD-N) to carry out Pearson correlation analysis, and two methods, Ridge regression and Partial Least Squares Regression (PLSR), were used to compare the accuracy of hyperspectral inversion of soil AHN content and pH value. Experiments have demonstrated that in the inversion of the AHN content prediction model, Ridge regression has a good modeling effect under the DWD-N model, where R²=0.647, RMSE=7.067mg/kg. PLSR has good prediction effect under DWD-N, where R² is the highest of 0.792, RMSE is 3.438mg/kg; in the model inversion of pH prediction, the full-band PLSR modeling effect of pH value under DWD pretreatment is the best, which modeling set and the prediction set of R² is 0.826 and 0.875, the RMSE is 0.217 mg/kg and 0.191 mg/kg respectively.

This study conducted field experiments in 2024 in Meidaizhao Town, Tumed Right Banner, Baotou City, Inner Mongolia Autonomous Region, adopting a plant-level sampling design with 10 maize plots selected as sampling areas (20 plants per plot). At four critical growth stages—jointing, heading, filling, and maturity—multimodal data, including that covering leaf spectra, root-zone soil spectra, and leaf chlorophyll and nitrogen content, were synchronously collected from each plant. In response to the prevalent limitations of the existing yield prediction methods, such as insufficient accuracy and limited generalization ability due to reliance on single-modal data, this study takes the acquired multimodal maize data as the research object and innovatively proposes a multimodal fusion prediction network. First, to handle the heterogeneous nature of multimodal data, a parallel feature extraction architecture is designed, utilizing independent feature extraction branches—leaf spectral branch, soil spectral branch, and biochemical parameter branch—to preserve the distinct characteristics of each modality. Subsequently, a dual-path feature fusion method, enhanced by a cross-attention mechanism, is introduced to enable dynamic interaction and adaptive weight allocation between cross-modal features, specifically between leaf spectra–soil spectra and leaf spectra–biochemical parameters, thereby significantly improving maize yield prediction accuracy. The experimental results demonstrate that the proposed model outperforms single-modal approaches by effectively leveraging complementary information from multimodal data, achieving an R² of 0.951, an RMSE of 8.68, an RPD of 4.50, and an MAE of 5.28. Furthermore, the study reveals that deep fusion between soil spectra, leaf biochemical parameters, and leaf spectral data substantially enhances prediction accuracy. This work not only validates the effectiveness of multimodal data fusion in maize yield prediction but also provides valuable insights for accurate and non-destructive yield prediction.