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学者姓名：王璨
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Abstract ：
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 degrees, 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.
Keyword ：
agricultural machinery agricultural machinery seedling dividing device seedling dividing device substrate block seedling substrate block seedling vegetable transplanter vegetable transplanter
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| GB/T 7714 | Zheng, Shuhe , Li, Jicheng , Dong, Zhenfa et al. Design and Experiments of Automatic Seedling Separation Device for Vegetable Substrate Block Seedling Transplanter [J]. | AGRICULTURE-BASEL , 2025 , 15 (4) . |
| MLA | Zheng, Shuhe et al. "Design and Experiments of Automatic Seedling Separation Device for Vegetable Substrate Block Seedling Transplanter" . | AGRICULTURE-BASEL 15 . 4 (2025) . |
| APA | Zheng, Shuhe , Li, Jicheng , Dong, Zhenfa , Wang, Jufei , Weng, Wuxiong , Cui, Zhichao et al. Design and Experiments of Automatic Seedling Separation Device for Vegetable Substrate Block Seedling Transplanter . | AGRICULTURE-BASEL , 2025 , 15 (4) . |
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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.
Keyword ：
edge device deployment edge device deployment ESG-YOLO ESG-YOLO tomato seedlings tomato seedlings transplanting quality detection transplanting quality detection
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| GB/T 7714 | Wu, Xinhui , Dong, Zhenfa , Wang, Can et al. ESG-YOLO: An Efficient Object Detection Algorithm for Transplant Quality Assessment of Field-Grown Tomato Seedlings Based on YOLOv8n [J]. | AGRONOMY-BASEL , 2025 , 15 (9) . |
| MLA | Wu, Xinhui et al. "ESG-YOLO: An Efficient Object Detection Algorithm for Transplant Quality Assessment of Field-Grown Tomato Seedlings Based on YOLOv8n" . | AGRONOMY-BASEL 15 . 9 (2025) . |
| APA | Wu, Xinhui , Dong, Zhenfa , Wang, Can , Zhu, Ziyang , Guo, Yanxi , Zheng, Shuhe . ESG-YOLO: An Efficient Object Detection Algorithm for Transplant Quality Assessment of Field-Grown Tomato Seedlings Based on YOLOv8n . | AGRONOMY-BASEL , 2025 , 15 (9) . |
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本发明涉及图像识别技术领域，尤其涉及一种基于TransNeXt的水下鱼类识别方法，包括：1)在MMSegmentation框架中对水下鱼群图像数据集进行预处理，划分为训练集和测试集；2)将基于Ade20K数据集上预训练好的改进TransNeXt模型及其参数迁移到MMSegmentation框架中；3)利用训练集和测试集对改进TransNeXt模型进行迭代训练；4)将待检测水下鱼类图像输入到已经训练好的改进TransNeXt模型中，输出识别的结果。本发明可以提高复杂水下环境中鱼类识别的精度，实现水下鱼类的准确识别，适宜进一步推广应用。
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| GB/T 7714 | 张洲铭 , 王璨 , 孔祥增 . 一种基于TransNeXt的水下鱼类识别方法 : CN202411617981.4[P]. | 2024-11-13 . |
| MLA | 张洲铭 et al. "一种基于TransNeXt的水下鱼类识别方法" : CN202411617981.4. | 2024-11-13 . |
| APA | 张洲铭 , 王璨 , 孔祥增 . 一种基于TransNeXt的水下鱼类识别方法 : CN202411617981.4. | 2024-11-13 . |
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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.
Keyword ：
computer vision computer vision environmental parameter control environmental parameter control growth stage detection growth stage detection instance segmentation instance segmentation lightweight lightweight
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| GB/T 7714 | Wang, Can , Wu, Xinhui , Wang, Zhaoquan et al. Real-Time Detection and Instance Segmentation Models for the Growth Stages of Pleurotus pulmonarius for Environmental Control in Mushroom Houses [J]. | AGRICULTURE-BASEL , 2025 , 15 (10) . |
| MLA | Wang, Can et al. "Real-Time Detection and Instance Segmentation Models for the Growth Stages of Pleurotus pulmonarius for Environmental Control in Mushroom Houses" . | AGRICULTURE-BASEL 15 . 10 (2025) . |
| APA | Wang, Can , Wu, Xinhui , Wang, Zhaoquan , Shao, Han , Ye, Dapeng , Kong, Xiangzeng . Real-Time Detection and Instance Segmentation Models for the Growth Stages of Pleurotus pulmonarius for Environmental Control in Mushroom Houses . | AGRICULTURE-BASEL , 2025 , 15 (10) . |
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This paper introduces a high current efficiency capacitor-less low dropout regulator (LDO) designed for low- power and consumer electronic applications, focusing on fast transient response and high power supply rejection (PSR) at high-frequency and heavy loads. The proposed adaptive Gm cell compensation (AGCC) technique replaces the traditional capacitor compensation, ensuring stability across the full load range while preserving bandwidth. The design resolves the trade-off between bandwidth and power consumption by integrating adaptive biasing and substrate driving techniques, achieving fast transient response and high PSR at high-frequency. The LDO operates with input voltages from 1.0 V to 1.2 V and an output voltage of 0.8 V. It maintains a quiescent current of 223 nA at 27 degrees C with no load. The current efficiency exceeds 88.5% and peaks at 99% for loads between 10 mu A and 20 mA. The worst-case post-layout simulation in a 40 nm CMOS process with an area of 0.001925 mm2 shows the PSR is below -17 dB and -37 dB at 1 MHz for 1 mA and 20 mA loads, with a figure of merit of 0.027 fs.
Keyword ：
Capacitor-less LDO Capacitor-less LDO Fast transient response Fast transient response High power supply rejection (PSR) High power supply rejection (PSR) Low-power Low-power
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| GB/T 7714 | Xie, Jin , Ye, Dapeng , Wang, Can et al. An adaptive Gm cell compensation technique for fast transient and high efficiency LDO without on/off-chip capacitor [J]. | AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS , 2024 , 190 . |
| MLA | Xie, Jin et al. "An adaptive Gm cell compensation technique for fast transient and high efficiency LDO without on/off-chip capacitor" . | AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 190 (2024) . |
| APA | Xie, Jin , Ye, Dapeng , Wang, Can , Li, Jinghu , Luo, Zhicong . An adaptive Gm cell compensation technique for fast transient and high efficiency LDO without on/off-chip capacitor . | AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS , 2024 , 190 . |
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介电常数与含水率有较高的相关性，可通过测量农产品的介电特性参数预测其干燥过程含水率，高压脉冲电场可有效加快干燥过程，但介电常数和含水率在此过程中的影响机理尚待进一步研究。为解决高压脉冲电场作用下果蔬介电常数与含水率之间关系的微观解释不明确问题，以苹果为研究对象，建立有效的苹果含水率与介电常数关系模型，三维模拟其内部电场参数，使用蒙特卡罗法对高压脉冲电场下苹果的介电特性进行理论模拟计算，把苹果内部宏观和微观介质作为集总参数来等效替代其内部整体结构。试验结果表明，当修正系数8.96时，控制苹果相对水含率从88%逐步下降到18%，介电常数计算值与实测值间相对误差稳定在10%以内。研究为脉冲电场处理果蔬加工提供理论依据。
Keyword ：
介电常数 介电常数 含水率 含水率 苹果 苹果 蒙特卡罗法 蒙特卡罗法 高压脉冲电场 高压脉冲电场
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| GB/T 7714 | 王璨 , 杜羿翰 , 刘振宇 . 苹果含水率与介电常数关系模型建构研究 [J]. | 农业工程 , 2023 , 13 (08) : 18-24 . |
| MLA | 王璨 et al. "苹果含水率与介电常数关系模型建构研究" . | 农业工程 13 . 08 (2023) : 18-24 . |
| APA | 王璨 , 杜羿翰 , 刘振宇 . 苹果含水率与介电常数关系模型建构研究 . | 农业工程 , 2023 , 13 (08) , 18-24 . |
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