In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO was improved based on the YOLOv8n model. Specifically, the model introduces deeply separable convolution (DWConv) into the backbone network, integrates the large separated convolution kernel attention mechanism (LSKA) and Slim-Neck structure into the Neck part, and adopts the DIoU loss function for optimization. The experimental results show that on the oyster mushroom dataset, the OMC-YOLO model had a higher detection effect compared to mainstream target detection models such as Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv6, YOLOv7-tiny, YOLOv8n, YOLOv9-gelan, YOLOv10n, etc., and that the mAP50 value reached 94.95%, which is an improvement of 2.62%. The number of parameters and the computational amount were also reduced by 26%. The model provides technical support for the automatic detection of oyster mushroom grades, which helps in realizing quality control and reducing labor costs and has positive significance for the construction of smart agriculture.

Apple-picking robots are increasingly applied in smart agriculture, but their performance is limited by complex orchard conditions such as unstable lighting, occlusion, and weather variations. This study proposes an optimized lightweight detection model, VBP-YOLO-prune, based on YOLOv8n, to enhance detection accuracy and deployment efficiency on edge devices. The model incorporates a V7 downsampling module, BiFPN feature fusion, and an improved PIOUv2 loss function, aiming to improve multi-scale representation and bounding box regression. A custom apple dataset was augmented with diverse lighting and weather conditions to improve generalization. Experimental results using 10-fold cross-validation show that VBP-YOLO-prune achieves 89.0 % mAP50 and 66.26 % mAP50–95, with Precision of 84.01 % and Recall of 80.52 %. Additionally, it reduces parameters by 79.7 %, FLOPs by 60.9 %, and increases FPS by 29.2 % compared to YOLOv8n.The final model contains only 0.61 M parameters, 3.2 GFLOPs, and runs at 102.6 FPS on NVIDIA Jetson Orin Nano. These results demonstrate that VBP-YOLO-prune provides a practical and efficient solution for real-time fruit detection in complex environments. Future research may extend this approach to other crop types and explore full integration into autonomous harvesting systems.

To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting.

Pleurotus ostreatus, classified under the phylum Basidiomycota, order Agaricales, and family Pleurotaceae, is a prevalent gray edible fungus. Its physical damage not only compromises quality and appearance but also significantly diminishes market value. This study proposed an enhanced method for detecting Pleurotus ostreatus damage based on an improved YOLOv8n model, aiming to advance the accessibility of damage recognition technology, enhance automation in Pleurotus cultivation, and reduce labor dependency. This approach holds critical implications for agricultural modernization and serves as a pivotal step in advancing China’s agricultural modernization, while providing valuable references for subsequent research. Utilizing a self-collected, self-organized, and self-constructed dataset, we modified the feature extraction module of the original YOLOv8n by integrating a lightweight GhostHGNetv2 backbone network. During the feature fusion stage, the original YOLOv8 components were replaced with a lightweight SlimNeck network, and an Attentional Scale Sequence Fusion (ASF) mechanism was incorporated into the feature fusion architecture, resulting in the proposed OMB-YOLO model. This model achieves a remarkable balance between parameter efficiency and detection accuracy, attaining a parameter of 2.24 M and a mAP@0.5 of 90.11% on the test set. To further optimize model lightweighting, the DepGraph method was applied for pruning the OMB-YOLO model, yielding the OMB-YOLO-tiny variant. Experimental evaluations on the damaged Pleurotus dataset demonstrate that the OMB-YOLO-tiny model outperforms mainstream models in both accuracy and inference speed while reducing parameters by nearly half. With a parameter of 1.72 M and mAP@0.5 of 90.14%, the OMB-YOLO-tiny model emerges as an optimal solution for detecting Pleurotus ostreatus damage. These results validate its efficacy and practical applicability in agricultural quality control systems.

In recent years, the research and application of ginseng, a famous and valuable medicinal herb, has received extensive attention at home and abroad. However, with the gradual increase in the demand for ginseng, discrepancies are inevitable when using the traditional manual method for grading the appearance and quality of ginseng. Addressing these challenges was the primary focus of this study. This study obtained a batch of ginseng samples and enhanced the dataset by data augmentation, based on which we refined the YOLOv8 network in three key dimensions: firstly, we used the C2f-DCNv2 module and the SimAM attention mechanism to augment the model’s effectiveness in recognizing ginseng appearance features, followed by the use of the Slim-Neck combination (GSConv + VoVGSCSP) to lighten the model These improvements constitute our proposed DGS-YOLOv8 model, which achieved an impressive mAP50 of 95.3% for ginseng appearance quality detection. The improved model not only has a reduced number of parameters and smaller size but also improves 6.86%, 2.73%, and 3.82% in precision, mAP50, and mAP50-95 over the YOLOv8n model, which comprehensively outperforms the other related models. With its potential demonstrated in this experiment, this technology can be deployed in large-scale production lines to benefit the food and traditional Chinese medicine industries. In summary, the DGS-YOLOv8 model has the advantages of high detection accuracy, small model space occupation, easy deployment, and robustness.