Reliable maturity detection of Sichuan pepper is critical for intelligent harvesting, precision agriculture, and automated quality assessment. However, existing vision-based methods often suffer from performance degradation in natural orchard conditions due to illumination variation, fruit occlusion, background interference, and substantial appearance diversity. In addition, the lack of dedicated Sichuan pepper maturity datasets limits the development and reliable evaluation of deep learning-based detection models. To address these challenges, this study proposes C-MorphYOLO (Circular-Morphological YO-LO), a morphology-aware detection framework for Sichuan pepper maturity recognition under field conditions. By incorporating circular morphological information into feature representation learning, the proposed framework enhances the perception of fruit structural characteristics and improves fine-grained maturity discrimination. Specifically, a Circular Depthwise Convolution Module (CDCM) is developed to strengthen boundary and shape-aware feature extraction, while a Feature Enhancement-based Upsampling Module (FEUM) and a Multi-Scale Adaptive Spatial Attention Gate (MASAG) are introduced to improve small-object representation and detection robustness. Furthermore, a natural-scene Sichuan pepper maturity dataset containing 2763 images and 2962 annotated instances was established for model training and comprehensive evaluation. Experimental results on the held-out validation set demonstrate that C-MorphYOLO achieves a Precision of 96.1% and an $\mathrm{m}\mathrm{A}{\mathrm{P}}_{50:95}$ of 89.1%, representing absolute improvements of 2.2% and 2.7%, respectively, compared with the YOLOv12s baseline. These results demonstrate that the proposed framework effectively improves maturity recognition and localization performance, providing a practical vision-based solution for intelligent Sichuan pepper harvesting and automated agricultural management.

Cage-raised chicken farming is influenced by multiple environmental and husbandry factors, making it prone to various types of abnormal eggs, such as in-cage eggs, broken eggs, dirty eggs, and undersized eggs. Automated coop inspections require simultaneous monitoring of chickens and eggs to ensure farming safety. However, computer vision-based inspection robots are susceptible to interference from complex backgrounds, cage mesh obstructions, and uneven lighting, making it difficult for their abnormal egg recognition accuracy to meet practical requirements. To overcome this problem, this study proposes an improved object detection network, EGG-DEIM: It employs a phased dynamic data augmentation strategy, utilizing different data augmentation techniques at different training stages. It incorporates Partial Dynamic Convolution (PDConv) modules to reduce redundant features in the backbone network while mitigating interference from cage mesh occlusion; introduces a Spatial-Frequency Modulation (SMF) module to enhance the representation of high-, medium-, and low-frequency features in eggs; and incorporates a Pyramid Sparse Transformer (PST) module for multi-scale feature fusion to address feature transmission discontinuities in occluded scenes, thereby enabling multi-class classification of eggs and improving detection accuracy. Experimental results on the collected caged-poultry dataset show that EGG-DEIM attains 92.6% mAP@50 and 77.5% mAP@50:95 with only 7.43 GFLOPs and 5.04 M parameters, achieving higher accuracy within a limited computational budget than the baseline and mainstream detectors of comparable size.