In complex orchard environments, sweet persimmons tend to grow in dense clusters and display similar coloration across different maturity stages, leading to heavy occlusion and poor inter-class color discriminability. To address these challenges, this paper presents RCPNet, a detection network tailored for such field conditions. The model integrates a Rectangular Self-Calibration Module (RCM) and a Context Feature Calibration Gating (CFCG) module. RCM strengthens axial context capture, while CFCG improves feature calibration; together they reduce local feature ambiguity and help reconstruct missing information in occluded regions. For distinguishing fruits at different ripening stages that share similar colors, a Parallelized Patch-aware Attention (PPA) detection head is adopted. By leveraging self-attention and multi-branch strategies, this head suppresses feature degradation and notably enhances sensitivity to color contrast. Experiments on sweet persimmon images show that RCPNet improves mean Average Precision (mAP) by 2.7 percentage points and mAP@0.5:0.95 by 3.7 percentage points over the baseline, reaching 93.6% detection accuracy for immature fruits. Ablation studies and comparisons with mainstream detectors indicate that the proposed model, though slightly heavier than lightweight detectors of analogous capacity, surpasses the accuracy of a larger small-scale counterpart and exhibits satisfactory robustness. Strong performance on a self-collected flat jujube dataset further confirms its generalization ability. The method delivers highly accurate detection for occluded and near-color fruits, providing technical support for precise fruit recognition and automated picking.