Sugarcane plays a vital role in many global economies, and its efficient cultivation is critical for sustainable development. A central challenge in sugarcane yield prediction and cultivation management is the precise segmentation of sugarcane fields from satellite imagery. This task is complicated by numerous factors, including varying environmental conditions, scale variability, and spectral similarities between crops and non-crop elements. To address these segmentation challenges, we introduce DSCA-PSPNet, a novel deep learning model with a unique architecture that combines a modified ResNet34 backbone, the Pyramid Scene Parsing Network (PSPNet), and newly proposed Dynamic Squeeze-and-Excitation Context (D-scSE) blocks. Our model effectively adapts to discern the importance of both spatial and channel-wise information, providing superior feature representation for sugarcane fields. We have also created a comprehensive high-resolution satellite imagery dataset from Guangxi’s Fusui County, captured on December 17, 2017, which encompasses a broad spectrum of sugarcane field characteristics and environmental conditions. In comparative studies, DSCA-PSPNet outperforms other state-of-the-art models, achieving an Intersection over Union (IoU) of 87.58%, an accuracy of 92.34%, a precision of 93.80%, a recall of 93.21%, and an F1-Score of 92.38%. Application tests on an RTX 3090 GPU, with input image resolutions of 512 × 512, yielded a prediction time of 4.57ms, a parameter size of 22.57MB, GFLOPs of 11.41, and a memory size of 84.47MB. An ablation study emphasized the vital role of the D-scSE module in enhancing DSCA-PSPNet’s performance. Our contributions in dataset generation and model development open new avenues for tackling the complexities of sugarcane field segmentation, thus contributing to advances in precision agriculture. The source code and dataset will be available on the GitHub repository https://github.com/JulioYuan/DSCA-PSPNet/tree/main.

Hyperspectral Image (HSI) classification categorizes each pixel into specific land-cover types. While Convolutional Neural Networks (CNNs) and Transformers have demonstrated commendable performance in HSI classification, they still face limitations in extracting high-resolution spatial and dense semantic spectral information from HSI data. This paper proposes a novel method with a dynamic routing mechanism called Spatial-Spectral Fusion BiFormer(SSFBF), which leverages the Transformer’s capability to model long-range dependencies with dynamic routing attention while incorporating CNN’s local receptive fields for fine-grained semantic features in HSI. Based on an “encoder-decoder” architecture, SSFBF employs Bi-Level Routing Attention (BRA) to capture high-resolution feature representations in the spatial domain and Single-Level Spectral Routing Attention(SSRA) to extract discriminative semantic information in the spectral domain. Additionally, we develop a Spatial-Spectral Position Feedforward Network (SSposFFN) with CNNs to mitigate local feature loss induced by attention, replacing the traditional MLP layer following BRA and SSRA. Furthermore, a novel Spatial-Spectral Dynamic Fusion Module (SSDF) is designed to integrate features in both the spatial and spectral domains. Various experiments on four HSI datasets have demonstrated that our SSFBF approach exhibits superior versatility on HSI datasets and outperforms the state-of-the-art HSI classification methods.

Amino acid property-aware phylogenetic analysis (APPA) refers to the phylogenetic analysis method based on amino acid property encoding, which is used for understanding and inferring evolutionary relationships between species from the molecular perspective. Fast Fourier transform (FFT) and Higuchi’s fractal dimension (HFD) have excellent performance in describing sequences’ structural and complexity information for APPA. However, with the exponential growth of protein sequence data, it is very important to develop a reliable APPA method for protein sequence analysis. Consequently, we propose a new method named FFP, it joints FFT and HFD. Firstly, FFP is used to encode protein sequences on the basis of the important physicochemical properties of amino acids, the dissociation constant, which determines acidity and basicity of protein molecules. Secondly, FFT and HFD are used to generate the feature vectors of encoded sequences, whereafter, the distance matrix is calculated from the cosine function, which describes the degree of similarity between species. The smaller the distance between them, the more similar they are. Finally, the phylogenetic tree is constructed. When FFP is tested for phylogenetic analysis on four groups of protein sequences, the results are obviously better than other comparisons, with the highest accuracy up to more than 97%. FFP has higher accuracy in APPA and multi-sequence alignment. It also can measure the protein sequence similarity effectively. And it is hoped to play a role in APPA’s related research.

Recently, Convolutional Neural Networks (CNNs) and Transformer have achieved considerable success in Hyperspectral Image (HSI) classification tasks. However, existing methods not only lack the study of spectral variability of samples from the same land class, but also struggle to mine local-global spectral information and spatial structure information of HSI at different granularities effectively. To mitigate these limitations, this article proposes a multigranularity spatial-spectral convolution Transformer network (MSSCFormer), which can reduce the intra-class spectral differences of samples and extract multigranularity spatial-spectral features from a local-global-local perspective. Specifically, MSSCFormer consists of three components: intra-class spectral attention (ICSA), spatial-spectral feature extractor (SSFE), and global-local convolutional Transformer (GLCT). Firstly, ICSA redistributes the spectral weights of samples of the same land class by establishing an attention mapping between spectral channels within the class to reduce the intra-class spectral differences. Second, SSFE extracts shallow spatial features and multigranularity spectral features of HSI with reassigned spectral weights samples from a local perspective. Finally, GLCT takes advantage of CNNs and Transformer, it uses MHSA to model global spectral features and utilizes local context feature block (LCFB) to capture local spatial features in a multigranularity way. Experimental results on three benchmark datasets show that MSSCFormer exhibits excellent performance on the HSI datasets and outperforms state-of-the-art HSI classification algorithms.