Research on UAV Online Visual Tracking Algorithm based on YOLOv5 and FlowNet2 for Apple Yield Inspection

Wang, Shaopeng; Zhang, Xiaodong; Shen, Haiming; Tian, Minxuan; Li, Mingyang

doi:10.1109/wrcsara57040.2022.9903925

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…On the other hand, for position correction, collision avoidance, and even target counting ( Wang S. et al, 2022 ), optical flow methods which compare consecutive frames to detect changes are used. This has the advantage of improving performance but comes usually with a reduction in resolution (since video frames have, unavoidably, less resolution than static images).…”

Section: Applicationsmentioning

confidence: 99%

“…The great majority of drone trajectories are out of a plane (images/videos are taken top-down while the drone is flying over the treetops). Still, there are some results such as in ( Wang S. et al, 2022 ) where the drone travels mid-row, through the orchard’s rows.…”

Section: Applicationsmentioning

confidence: 99%

“…Not least, for the monitoring of crops in precision agriculture, collaboration with wireless ground sensor networks is of particular importance ( Popescu et al., 2020 ). On the other hand, in complex applications related to orchard monitoring, UAVs have the advantage to take images from either a medium distance (10 m -100 m) through an appropriate design of the trajectories - such as in the case of orchard or tree segmentation ( Adamo et al., 2021 ; Akca and Polat, 2022 ) or to determine the water stress index ( Zhang C. et al., 2021 ) or from a smaller distance (tens of cm) - such as the case of detecting harmful insects ( Aota et al., 2021 ; Ichim et al., 2022 ) or fruits ( Wang S. et al., 2022 ). The UAVs compared to terrestrial robots is also a more flexible and less expensive solution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Popescu,

Ichim,

Stoican

2023

Front. Plant Sci.

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Orchard monitoring is a vital direction of scientific research and practical application for increasing fruit production in ecological conditions. Recently, due to the development of technology and the decrease in equipment cost, the use of unmanned aerial vehicles and artificial intelligence algorithms for image acquisition and processing has achieved tremendous progress in orchards monitoring. This paper highlights the new research trends in orchard monitoring, emphasizing neural networks, unmanned aerial vehicles (UAVs), and various concrete applications. For this purpose, papers on complex topics obtained by combining keywords from the field addressed were selected and analyzed. In particular, the review considered papers on the interval 2017-2022 on the use of neural networks (as an important exponent of artificial intelligence in image processing and understanding) and UAVs in orchard monitoring and production evaluation applications. Due to their complexity, the characteristics of UAV trajectories and flights in the orchard area were highlighted. The structure and implementations of the latest neural network systems used in such applications, the databases, the software, and the obtained performances are systematically analyzed. To recommend some suggestions for researchers and end users, the use of the new concepts and their implementations were surveyed in concrete applications, such as a) identification and segmentation of orchards, trees, and crowns; b) detection of tree diseases, harmful insects, and pests; c) evaluation of fruit production, and d) evaluation of development conditions. To show the necessity of this review, in the end, a comparison is made with review articles with a related theme.

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%