Research on Drought Stress Detection in the Seedling Stage of Yunnan Large-Leaf Tea Plants Based on Biomimetic Vision and Chlorophyll Fluorescence Imaging Technology

To address the problem of drought stress detection in the seedling stage of Yunnan large-leaf tea plants, this study proposes an improved network, MC-YOLOv13-L, based on YOLOv13. 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\u2019 \multi-scale parallelism\u2014global modulation\u2014long-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 experimental results show that the Box Loss, Cls Loss and DFL Loss of MC-YOLOv13-L on the training set are decreased by 5.08 %, 3.13 % and 4.85 % respec-tively compared with YOLOv13, and decreased by 2.82 %, 7.32 % and 3.51 % respectively on the verification set. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP by 4.64 %, 6.93 % and 4.2 % respectively on the basis of only sacrificing 0.63 FPS. The network can not only quickly and accurately identify the drought stress response of tea plants, but also provide a reliable technical basis for the intelligentization of tea production, and also provide a useful reference for the practical application and transformation of bio-heuristic computing in complex ecosystems.