#zeppelin UAV

Precision agriculture has emerged as a technological strategy to enhance the efficiency of agricultural systems through the integration of digital tools and automated processes. In this context, unmanned aerial vehicles (UAVs) have gained increasing relevance due to their ability to optimize agrochemical application, reduce operational costs, and minimize environmental impacts. The objective of this research was to conceptually design a zeppelin-type unmanned aerial vehicle for spraying corn crops covering areas of up to one hectare. The study adopted an applied research approach with a descriptive scope and a non-experimental research design, supported by a comprehensive documentary review and the application of the Jenkins methodology for the systematic development of the proposed prototype. Key design variables included payload capacity, flight endurance, spraying coverage, aerodynamic stability, and operational efficiency, while also considering applicable aeronautical regulations. The proposed design demonstrates potential advantages over conventional spraying methods by reducing application time, improving the efficiency of agrochemical use, decreasing operator exposure to hazardous substances, and increasing agricultural productivity. It is concluded that the incorporation of a zeppelin-type UAV constitutes a viable technological alternative to strengthen precision agriculture, promote sustainable farming practices, and support the modernization of agricultural activities, particularly for small-scale producers.

提出异构多无人机覆盖路径规划框架HCB，通过细粒度路径精化与成本平衡提升大规模区域覆盖效率。

综述无人机应用潜力与发展趋势，强调其向自主化、网络化、数据驱动系统演进。

系统梳理我国农业低空飞行平台、智能控制、低空通信与多机协同等关键技术及植保、遥感、转运等场景进展，指出需突破智能化作业与标准规范瓶颈。

Abstract Context Spatially explicit and accurate maize crop height maps are essential for supporting site-specific management in precision agriculture. Although low-cost unmanned aerial system (UAS)-based multispectral imagery offers a promising approach for maize crop height estimation, it’s accuracy under varying canopy conditions remains uncertain. Aims In this study, we used 3D point clouds to estimate maize crop height during sparse and dense canopy cover based on UAS-captured multispectral images and compare with light detection and ranging (LiDAR)-based estimates. Methods The 3D point clouds from multispectral images and LiDAR data were processed using a spatially localized grid (SLG) filtering approachto derive digital terrain model (DTM) and canopy surface model (CSM), which were then used to estimate maize crop height.Furthermore, two strategies were explored — (1) grid expansion, and (2) using ground reference derived during sparse canopy aspotential tools to mitigate issues during dense canopy cover for multispectral image-based estimations. Results Results showed that the crop height estimated from multispectral images during sparse canopy cover show comparable accuracy (r = 0.9, nRMSE = 32%) to LiDAR-based estimation (r = 0.81, nRMSE = 17%). The accuracy of multispectral-based estimation, however, significantly declined under dense canopy cover, where no meaningful correlation was observed (r = − 0.1, nRMSE = 61%). In contrast, LiDAR-based estimates show consistent level of accuracy under the same conditions (r = 0.78, nRMSE = 15%). When grid expansion strategy was implemented for multispectral-based estimation during the dense canopy cover, the nRMSE was observed to decrease up to 28%. Meanwhile, with the use of sparse canopy cover ground reference, the nRMSE decreased up to 17%, comparable to the LiDAR-based estimate. Conclusion Overall, these results demonstrate the potential of deriving crop heights cost-effectively using multispectral imaging from UAS. This approach offers a scalable and economically viable alternative to expensive LiDAR systems, enabling wider integration of crop height information into precision agriculture applications.

Corn canopy structure and biomass assessment using UAV-based monocular depth and structure-from-motion。Smart Agricultural Technology

集成里程计与视觉数据，实现无GPS环境下无人机自主导航与基础设施监测。

综述水下光学图像中的小目标检测，梳理问题、数据集、算法，并展望未来方向。