Qingzhen Zhu

Advances in Recognition Methods for Fruit and Vegetable Harvesting

Dianlei Han, Shixing Xu, Siyu Zhou, Qingzhen Zhu et autres

The harvesting of fruit and vegetable crops has long been plagued by prominent issues such as high labor costs, low harvesting efficiency, and high fruit damage rates. The application of object recognition technology has enabled harvesting robots to identify, detect, and locate …

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Design and Testing of a Gravity-Constrained Fertilizer Guide Tube for Stable Fertilizer Cluster Movement

Xinhe Shan, Jianjun Dong, Bingxin Yan, Liwei Li et autres

Fertilizer clusters tend to lose their agglomerated state upon ground contact, which reduces fertilizer use efficiency. To address this, we designed a gravity-constrained fertilizer guide tube. Based on the principle of minimum friction, we designed a trapezoidal groove. By analyzing the motion …

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Maize seedling center detection based on instance segmentation and skeleton-driven geometric morphological analysis

Jiahang Pan, Xingyu Ban, Yuanzhi Xu, Qingzhen Zhu et autres

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Improving Out-of-Distribution Robustness for Wheat Head Detection: A Lightweight Modified YOLOv13 Approach

Peng Hui, Liyuan Zhang, Qingzhen Zhu

Wheat head detection is a critical component in high-throughput phenotyping, holding significant application value for wheat yield estimation and breeding analysis. With the continuous advancement of general object detection models, state-of-the-art detectors achieve high accuracy in same-distribution wheat head detection scenarios. However, …

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Path planning for agricultural harvesting manipulators under environmental uncertainty: Optimization objectives, engineering trade-offs, and learning-based adaptation

Jiamuyang Zhao, Yang Yu, Guangwei Wu, Liping Chen et autres

Transitional-Class Collapse in Frozen Vision-Language Models: Few-Shot, Training-Free Ordinal Decision Correction for Selective Recognition

Jiamuyang Zhao, Yihao Yang, Yanqiu Zhu, Dai Xu et autres

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Recent developments and applications of weed detection, invasion prediction and early warning: A review

Shuai He, Chengliang Liu, Liang Gong, Qingzhen Zhu et autres

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Tobacco yield estimation via multi-source data fusion and recurrent neural networks

Mingzheng Zhang, Baoyuan Zhang, Chunjiang Zhao, Liping Chen et autres

In China, tobacco production must strictly follow the yield plan set by the higher authorities. In this context, accurate and stable yield estimation is meaningful for effective production management. In this paper, we adopted a multi-source data fusion strategy to develop the …

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Development of an Orchard Inspection Robot: A ROS-Based LiDAR-SLAM System with Hybrid A*-DWA Navigation

Jiwei Qu, Yan Gu, Kangquan Guo, Qingzhen Zhu

The application of orchard inspection robots has become increasingly widespread. How-ever, achieving autonomous navigation in unstructured environments continues to pre-sent significant challenges. This study investigates the Simultaneous Localization and Mapping (SLAM) navigation system of an orchard inspection robot and evaluates its performance …

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Correction: Zhu et al. Maize Seed Variety Classification Based on Hyperspectral Imaging and a CNN-LSTM Learning Framework. Agronomy 2025, 15, 1585

Qingzhen Zhu, Quancheng Liu, Dandan Ma, Yanqiu Zhu et autres

In the original publication [...]

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Experimental Investigation of Impact Mechanisms of Seeding Quality for Ridge-Clearing No-Till Seeder Under Strip Tillage

Yuanyuan Gao, Yongyue Hu, Shuo Yang, Xueguan Zhao et autres

Under conservation tillage in the Huang-Huai-Hai wheat–maize rotation area, the ridge-clearing no-till seeder for strip tillage mitigates the adverse impacts of surface residues on seeding quality by clearing stubble specifically within the seed rows, demonstrating significant potential for application and promotion. However, …

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Tea bud detection in complex natural environments based on YOLOv8n-RGS

Siquan Li, Fangzheng Gao, Quan Sun, Jiacai Huang et autres

Abstract To address the challenge of accurately detecting tender tea buds under natural conditions due to occlusion, uneven lighting, and missed small targets, this study proposes a lightweight detection method called YOLOv8n-RGS, based on YOLOv8n. The method focuses on small object detection …

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