Colletotrichum capsici is an important pathogen causing anthracnose in postharvest peppers in parts of Asia, seriously compromising quality and storage life. Unveiling the pathogenic mechanism can better prevent postharvest disease in pepper. This study investigated the impacts of C. capsici infection on cell wall and phenylpropanoid metabolism in postharvest pepper. Compared to the non-inoculated peppers, C. capsici infection notably increased the disease index, damaged visual quality, and reduced the firmness. Morphological observations showed that C. capsici infection contributed to the collapse of epidermal cell structure. During the early stage, C. capsici triggered pepper’s defensive responses, including lignin deposition around the wounds, increased cellulose and hemicellulose content, and boosted disease-resistance enzymes, including phenylalanine ammonia-lyase (PAL), cinnamic acid 4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), cinnamyl alcohol dehydrogenase (CAD), laccase (LAC), β-1,3-glucanase (β-1,3-Glu), and chitinase (CHI), alongside elevated total phenolics and flavonoids. However, as storage time progressed, the activities of carboxymethy cellulase (Cx), polygalacturonase (PG), pectin methylesterase (PME), and β-glucosidase (β-Glu) remained at a high level, leading to a reduction in cell wall components, a decline in the activities of disease-resistance enzymes, and a decrease in phenylpropanoid metabolite, resulting from disease progression in pepper. These insights highlight the need for early intervention strategies to mitigate postharvest losses by targeting pathogen-induced stress responses and cell wall integrity preservation.

This study addresses the challenges of passion fruit visual maturity-class detection in complex natural orchard environments, particularly the missed detections of visually challenging fruit instances caused by background interference, occlusion, and illumination variations. To address this issue, an improved detection model, WEC-YOLO11, is proposed based on YOLO11n. The model incorporates the Efficient Channel Attention (ECA) mechanism to enhance discriminative feature representation while suppressing background interference, introduces the Content-Aware ReAssembly of Features (CARAFE) operator for content-aware upsampling, and adopts the WIoUv3 loss function with a dynamic non-monotonic focusing mechanism for bounding box regression. Across three independent runs, WEC-YOLO11 achieved an average mean average precision (mAP)@0.5 of 86.2% and an average recall of 81.1%, with 2.69 M parameters and 6.5 giga floating-point operations per second (GFLOPs). Compared to YOLO11n, the recall of ripe passion fruit (rpf) increased from 76.5% to 79.9% (+3.4 percentage points), while rpf precision showed a slight decrease from 77.6% to 77.1%, indicating a recall-oriented precision–recall trade-off. The overall recall increased from 79.9% to 81.1% (+1.2 percentage points). Under the tested batch-1 RTX 4090 setting, the measured throughput was 91.6 frames per second (FPS). These results indicate a favorable accuracy–complexity trade-off for passion fruit visual maturity-class detection within the evaluated public orchard dataset.