Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability of the underlying backbone network. Secondly, the MDPM module is proposed to generate attention matrices by capturing the horizontal and vertical information of feature maps, reducing duplicate information in the feature maps. Finally, we will replace the upsampling module of YOLOv8 with the CARAFE module to provide better upsampling performance. Extensive experiments on two publicly available datasets showed that the F1, mAP₅₀ and mAP₇₅ metrics improved by 1.2%, 5.1%, 2.9% and 3.8%, 1.3%, 2.2%, respectively, compared to the baseline model. This study showcases the algorithm’s potential for practical applications in weed detection within cotton fields, promoting the significant development of artificial intelligence in the field of agriculture.

Speech emotion recognition (SER) is a key branch in the field of artificial intelligence, focusing on the analysis and understanding of emotional content in human speech. It involves a multidisciplinary knowledge of acoustics, phonetics, linguistics, pattern recognition, and neurobiology, aiming to establish a connection between human speech and emotional expression. This technology has shown broad application prospects in the medical, educational, and customer service fields. With the evolution of deep learning and neural network technologies, SER research has shifted from relying on manually designed low-level descriptors (LLDs) to utilizing complex neural network models for extracting high-dimensional features. A perennial challenge for researchers has been how to comprehensively capture the rich emotional features. Given that emotional information is present in both time and frequency domains, our study introduces a novel time–frequency domain convolution module (TFCM) based on Mel-frequency cepstral coefficient (MFCC) features to deeply mine the time–frequency information of MFCCs. In the deep feature extraction phase, for the first time, we have introduced hybrid dilated convolution (HDC) into the SER field, significantly expanding the receptive field of neurons, thereby enhancing feature richness and diversity. Furthermore, we innovatively propose the residual attention-gated multilayer perceptron (RA-GMLP) structure, which combines the global feature recognition ability of GMLP with the concentrated weighting function of the multihead attention mechanism, effectively focusing on the key emotional information within the speech sequence. Through extensive experimental validation, we have demonstrated that TFCM, HDC, and RA-GMLP surpass existing advanced technologies in enhancing the accuracy of SER tasks, fully showcasing the powerful advantages of the modules we proposed.