YOLO-AVCA-CBAMNet: Attention-driven framework for detection and classification of green pepper maturity stages

. The findings demonstrate the potential of attention-driven models to support interpretable, efficient, and scalable maturity assessment solutions in precision agriculture.•Developed an end-to-end framework "YOLO-AVCA-CBAMNet" integrating object detection and attention-driven classification for pepper maturity assessment in natural field conditions.•Employed a field-derived image dataset of pepper berries collected under naturally varying illumination and environmental conditions, thereby supporting the ecological validity and practical relevance of the proposed maturity assessment approach.•Incorporated complementary attention mechanisms-AVCAM to enhance global contextual representation and CBAM to refine spatial and channel-specific feature responses-thereby improving discrimination among visually similar maturity stages.