Neuro-Heuristic Computational Intelligence Approach for Optimization of Electro-Magneto-Hydrodynamic Influence on a Nano Viscous Fluid Flow

Butt, Zeeshan Ikram; Ahmad, Iftikhar; Raja, Muhammad Asif Zahoor; Hussain, Syed Ibrar; Shoaib, Muhammad; Ilyas, Hira

doi:10.1155/2023/7626478

Cited by 3 publications

(1 citation statement)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the continuous development of deep learning technology, it has shown great application potential in several fields. Its applications have been extended to IoT security detection, management of traffic congestion problems at urban intersections [7], supply chain management procurement, inventory control, Wi-Fi channel state information to recognize human activities [8], tomato identification and localization [9], real-time detection of crop pests and diseases [10][11][12], forest fire smoke detection to optimize the efficiency of agricultural operations, classification of sonar images [13], and effects of electromagnetic hydrodynamics on nano-viscous fluid flow [14]. These studies not only highlight the promise of deep learning techniques in various fields but also demonstrate the value of their wide range of applications in the agricultural industry.…”

Section: Introductionmentioning

confidence: 99%

TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention

Chen,

Zhou,

Lin

et al. 2024

Agronomy

View full text Add to dashboard Cite

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.

show abstract