Tomato-Adaptive Attention YOLOv8 for Accurate and Interpretable Maturity Detection Across Diverse Environments

Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems.