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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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preprints
2018
置信度 0.74
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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preprints
2018
置信度 0.74
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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europepmc
2026
置信度 0.80
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices into data-driven, intelligent production paradigms. Within our proposed Perception-Decision-Execution (PDE) framework, this paper reviews AI technology advances fr…
europepmc
2026
置信度 0.80
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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europepmc
2025
置信度 0.80
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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Abstract To address the limited robustness of single-sensor detection in complex environments, this paper proposes a cooperative search algorithm for unmanned aerial vehicle (UAV) swarm based on heterogeneous sensor fusion (HS-CS). The algorithm leverages the …
europepmc
Jingzhi Guo, Zhe Li, Zhihao Zhang, Ning Wang 等
2026
置信度 0.80
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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Abstract Spatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and te…
preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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europepmc
2025
置信度 0.80
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preprints
2026
置信度 0.74
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preprints
2026
置信度 0.74
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Abstract High-throughput phenotyping using unmanned aerial vehicle (UAV) multispectral imagery offers a promising approach for predicting wheat yields under variable sowing conditions. This study evaluated the effectiveness of UAV-based vegetation indices comp…
preprints
2026
置信度 0.74
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europepmc
2026
置信度 0.80
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Unmanned aerial vehicle decision-making issues are increasingly being addressed using reinforcement learning (RL) (UAVs). The current advances in RL-based algorithms for UAV applications, encompassing both single-agent and swarm scenarios, are thoroughly revie…
crossref
Ummey Habiba, Roshan Jahan
2023-05-10T19:51:40Z
置信度 0.70
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crossref
2016-10-13T11:57:09Z
置信度 0.70
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crossref
2024-01-10T17:03:06Z
置信度 0.70
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Drone technology is undoubtedly part of our everyday life these days. The business and industrial use of unmanned aerial vehicles (UAV) can provide favorable solutions in many areas of life, and they are also great for emergency situations and for reaching har…
crossref
István Nagy, Edit Laufer
2024-07-19T02:26:01Z
置信度 0.70
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This dissertation focuses on the path planning of unmanned aerial vehicle (UAV) swarms under distributed and hybrid control scenarios. It presents two such models and analyzes them both from theory and practice. In the first method, a distributed formation con…
crossref
Srijita Mukherjee
2025-01-26T10:16:59Z
置信度 0.70
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crossref
Kazım Duraklar
2024-10-29T04:41:47Z
置信度 0.70