Table 1_TB-YOLOv8n: an improved YOLOv8n-based model for tip-burn detection in plant factory-grown pakchoi under LED lighting.docx

Image-based detection of small or mild visible tip-burn symptoms remains challenging because the affected regions are usually small, irregularly shaped, low in color contrast, and easily disturbed by red-blue LED lighting, leaf overlap, curled leaf margins, and heart-leaf occlusion.</p>Methods<p>To address these challenges, this study constructed a pakchoi tip-burn image dataset under plant factory conditions and proposed TB-YOLOv8n, a lightweight detection model based on YOLOv8n.

AFGCAttention was introduced at the end of the backbone to enhance informative channel responses related to subtle tip-burn regions and suppress redundant background information. GLSA was embedded between the backbone and neck to integrate local texture details with global spatial context under leaf overlap and low-contrast symptom conditions. In addition, a P2-enhanced BiFPN neck was constructed to strengthen cross-scale feature fusion and preserve fine spatial information for small visible tip-burn regions.</p>Results<p>Experimental results showed that TB-YOLOv8n achieved a precision of 89.4%, a recall of 87.6%, an mAP@0.5 of 93.2% ± 0.3%, and an mAP@0.5–0.95 of 59.0% ± 0.3%, which were 4.2, 3.9, 3.4, and 10.0 percentage points higher than those of the original YOLOv8n, respectively.