Intelligent detection of tomato seedling transplant quality represents a core technology for advancing agricultural automation. However, in practical applications, existing algorithms still face numerous technical challenges, particularly with prominent issues of false detections and missed detections during recognition. To address these challenges, we developed the ESG-YOLO object detection model and successfully deployed it on edge devices, enabling real-time assessment of tomato seedling transplanting quality. Our methodology integrates three key innovations: First, an EMA (Efficient Multi-scale Attention) module is embedded within the YOLOv8 neck network to suppress interference from redundant information and enhance morphological focus on seedlings. Second, the feature fusion network is reconstructed using a GSConv-based Slim-neck architecture, achieving a lightweight neck structure compatible with edge deployment. Finally, optimization employs the GIoU (Generalized Intersection over Union) loss function to precisely localize seedling position and morphology, thereby reducing false detection and missed detection. The experimental results demonstrate that our ESG-YOLO model achieves a mean average precision mAP of 97.4%, surpassing lightweight models including YOLOv3-tiny, YOLOv5n, YOLOv7-tiny, and YOLOv8n in precision, with improvements of 9.3, 7.2, 5.7, and 2.2%, respectively. Notably, for detecting key yield-impacting categories such as “exposed seedlings” and “missed hills”, the average precision (AP) values reach 98.8 and 94.0%, respectively. To validate the model’s effectiveness on edge devices, the ESG-YOLO model was deployed on an NVIDIA Jetson TX2 NX platform, achieving a frame rate of 18.0 FPS for efficient detection of tomato seedling transplanting quality. This model provides technical support for transplanting performance assessment, enabling quality control and enhanced vegetable yield, thus actively contributing to smart agriculture initiatives.

Environmental control based on growth stage is critical for enhancing the yield and quality of industrially cultivated Pleurotus pulmonarius. Challenges such as scene complexity and overlapping mushroom clusters can impact the accuracy of growth stage detection and target segmentation. This study introduces a lightweight method called the real-time detection model for the growth stages of P. pulmonarius (GSP-RTMDet). A spatial pyramid pooling fast network with simple parameter-free attention (SPPF-SAM) was proposed, which enhances the backbone’s capability to extract key feature information. Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. The class-aware adaptive feature enhancement (CARAFE) upsampling module is utilized to enhance instance segmentation performance. This study innovatively fusions the improved methods, enhancing the feature representation and the accuracy of masks. By lightweight model design, it achieves real-time growth stage detection of P. pulmonarius and accurate instance segmentation, forming the foundation of an environmental control strategy. Model evaluations reveal that GSP-RTMDet-S achieves an optimal balance between accuracy and speed, with a bounding box mean average precision (bbox mAP) and a segmentation mAP (segm mAP) of 96.40% and 93.70% on the test set, marking improvements of 2.20% and 1.70% over the baseline. Moreover, it boosts inference speed to 39.58 images per second. This method enhances detection and segmentation outcomes in real-world environments of P. pulmonarius houses, offering a more accurate and efficient growth stage perception solution for environmental control.

To address the critical challenges of low success rates and high seedling damage in automatic transplanters for vegetable substrate block seedlings, this study took cabbage substrate block seedlings as the research object and designed a silica gel wheel–synchronous belt clamping seedling separation device. An experimental platform was constructed to perform a three-factor, three-level orthogonal test, investigating the effects of the wheelbase of the silica gel wheel, the inclination angle of the conveyor belt, and the wheelbase of the silica gel wheel and the synchronous belt on seedling separation success rate and substrate block breakage rate. A quadratic regression model was established to analyze the influence of each factor on the index and to optimize the parameter combination verification test. The results showed that the seedling separation effect was better when the wheelbase of the silica gel wheel was 60.47 mm, the inclination angle of the conveyor belt was 8.67°, and the wheelbase of the silica gel wheel and seedling separation synchronous belt was 39.8 mm. The success rate of seedling separation was 90.21% and the substrate block breakage rate was 6.88% in the field verification test of this parameter combination. When the operating speed is 60 plants/min, there is a higher success rate of seedling separation and a lower substrate block breakage rate. This study explored the conditions for stable seedling separation using the seedling separation device, and provided practical reference for the study of the automatic seedling separation of substrate block seedlings.

Accurate monitoring of fish morphology and behavior is crucial to intelligent aquaculture management, especially for economically important species such as the large yellow croaker. To address challenges including turbid water, uneven lighting, and occlusions in real farming environments, this study develops DMSNet—an underwater image segmentation model based on an enhanced TransNeXt architecture. The model incorporates three novel modules, a Convolutional Dynamic Gated Linear Unit (CDGLU), an Agentic Cross-Attention Fusion Module (ACAF), and Pooling Channel-Spatial Attention (PCSA), significantly improving feature fusion and robustness under complex conditions. To better support applications in aquaculture, a dedicated dataset of underwater large yellow croaker, called the Large Yellow Croaker Dataset (LYCD), was constructed, covering the varied clarity levels typically found in farming operations. Experimental results show that DMSNet achieves an Acc of 98.01%, an IoU of 91.73%, an F1 of 96.17%, and an inference speed of 29.25 FPS, outperforming state-of-the-art methods, particularly in turbid and low-contrast scenarios. This study presents a practical and efficient tool for non-invasive fish monitoring that is capable of accurately identifying large yellow croaker underwater in real-world aquaculture environments with complex water conditions.