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.

In the context of blueberry orchard management and automated harvesting, this study introduces an improved YOLOv8 model, ADE-YOLO, designed for precise blueberry ripeness detection, enhancing automated picking efficiency. Built on the YOLOv8n architecture, ADE-YOLO features a dimensionality-reducing convolution at the backbone’s end, reducing computational complexity while optimizing input features. This improvement enhances the effectiveness of the AIFI module, particularly in multi-scale feature fusion, boosting detection accuracy and robustness. Additionally, the neck integrates a dynamic sampling technique, replacing traditional upsampling methods, allowing for more precise feature integration during feature transfer from P5 to P4 and P4 to P3. To further enhance computational efficiency, CIOU is replaced with EIOU, simplifying the aspect ratio penalty term while maintaining high accuracy in bounding box overlap and centroid distance calculations. Experimental results demonstrate ADE-YOLO’s strong performance in blueberry ripeness detection, achieving a precision of 96.49%, recall of 95.38%, and mAP scores of 97.56% (mAP50) and 79.25% (mAP50-95). The model is lightweight, with just 2.95 M parameters and a 6.2 MB weight file, outpacing YOLOv8n in these areas. ADE-YOLO’s design and performance underscore its significant application potential in blueberry orchard management, providing valuable support for precision agriculture.

The maturity of Lentinus edodes directly affects its quality, taste, and market value. Currently, maturity assessment primarily relies on manual experience, making it difficult to ensure efficiency and consistency. To achieve efficient and accurate detection of Lentinus edodes maturity, this study proposes an improved lightweight object detection model, YOLOv8n-CFS. Based on YOLOv8n, the model integrates the SegNeXt Attention structure to enhance key feature extraction capabilities and optimize feature representation. A Feature Diffusion Propagation Network (FDPN) is designed to improve the expressive ability of objects at different scales through cross-layer feature propagation, enabling precise detection. The CSFCN module combines global cue reasoning with fine-grained spatial information to enhance detection robustness and generalization performance in complex environments. The CWD method is adopted to further optimize the model. Experimental results demonstrate that the proposed model achieves 97.34% mAP50 and 84.5% mAP95 on the Lentinus edodes maturity detection task, representing improvements of 2.02% and 4.92% compared to the baseline method, respectively. It exhibits excellent stability in five-fold cross-validation and outperforms models such as Faster R-CNN, YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv10n, YOLOv11n, and YOLOv12. This study provides efficient and reliable technical support for Lentinus edodes maturity detection and holds significant implications for the intelligent production of edible fungi.