<![CDATA[Early identification of tea leaf diseases is essential for sustaining crop productivity and preventing significant yield losses, making accurate automated detection a critical requirement in modern agricultural management. This study aims to improve the robustness of YOLOv8 for disease detection by integrating two complementary optimization modules chosen for their suitability in addressing common challenges in plant imagery: the Convolutional Block Attention Module (CBAM), which enhances discriminative feature focus under complex visual noise, and the Bidirectional Feature Pyramid Network (BiFPN), which strengthens multi-scale feature fusion to capture small or low-contrast lesions. The target diseases include Algal Leaf Spot, Brown Blight, and Grey Blight, using a combined dataset of primary field images and secondary data from Kaggle. Four models were developed—YOLOv8n (baseline), YOLOv8-CBAM, YOLOv8-BiFPN, and YOLOv8-CBAM-BiFPN. Experimental results demonstrate consistent performance gains across all enhanced variants. The baseline model obtained a precision of 0.760, recall of 0.735, and mAP50 of 0.793. Incorporating CBAM increased precision to 0.824 and recall to 0.780, while BiFPN yielded the highest recall of 0.820 with superior multi-scale generalization. The combined CBAM-BiFPN model achieved the strongest overall results, with a precision of 0.879, recall of 0.814, mAP50 of 0.886, and mAP50–90 of 0.739. These findings indicate that integrating CBAM and BiFPN substantially enhances YOLOv8’s capability in complex leaf-disease scenarios and offers practical potential for deployment in real agricultural settings to support faster decision-making and more effective disease management.]]>
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