Artificial Intelligence-Based Bio-Inspired Computer Vision System

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 857

Special Issue Editors


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Guest Editor
School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: computer vision; deep learning; multimodal learning

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Guest Editor
Computer and Information Sciences, Northumbria University, Ellison Pl, Newcastle upon Tyne NE1 8ST, UK
Interests: deep learning; multimodal learning

Special Issue Information

Dear Colleagues,

The rapid advancement of artificial intelligence (AI), particularly in deep learning and bio-inspired computing, has profoundly reshaped the field of computer vision. Unlike conventional vision systems that rely on handcrafted features, bio-inspired approaches draw inspiration from natural perceptual and cognitive mechanisms, enabling more efficient, adaptive, and generalizable solutions. Integrating such biologically grounded principles with state-of-the-art AI architectures is not only key to understanding visual perception but also crucial for building next-generation intelligent systems.

With the emergence of multimodal learning, embodied intelligence, and neuromorphic computing, computer vision is entering a transformative stage where perception, reasoning, and interaction are increasingly intertwined. However, research is still fragmented across domains—ranging from neural-inspired architectures to cross-modal fusion and embodied visual understanding.

This Special Issue aims to consolidate these efforts, provide a platform for interdisciplinary collaboration, and advance the development of robust, explainable, and scalable AI-based bio-inspired vision systems that can better bridge the gap between biological intelligence and artificial perception.

Dr. Haoran Duan
Dr. Bing Zhai
Guest Editors

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Keywords

  • artificial intelligence
  • bio-inspired computing
  • artificial intelligence
  • bio-inspired computing
  • computer vision
  • deep learning
  • multimodal intelligence
  • embodied intelligence
  • neuromorphic computing
  • cognitive systems
  • explainable AI
  • intelligent robotics

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Published Papers (1 paper)

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Research

24 pages, 3255 KB  
Article
Research on Drought Stress Detection in the Seedling Stage of Yunnan Large-Leaf Tea Plants Based on Biomimetic Vision and Chlorophyll Fluorescence Imaging Technology
by Baijuan Wang, Weihao Liu, Xiaoxue Guo, Jihong Zhou, Xiujuan Deng, Shihao Zhang and Yuefei Wang
Biomimetics 2026, 11(1), 56; https://doi.org/10.3390/biomimetics11010056 - 8 Jan 2026
Viewed by 556
Abstract
To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. [...] Read more.
To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Bio-Inspired Computer Vision System)
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