A Drone-based Application for Scouting Halyomorpha halys Bugs in Orchards with Multifunctional Nets

Sorbelli, Francesco Betti; Coró, Federico; Das, Sajal K.; Bella, Emanuele Di; Palazzetti, Lorenzo; Pinotti, Cristina M.

doi:10.1109/percomworkshops53856.2022.9767309

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…Drones have multifaceted applications in the field of agriculture. It is now recommended to use in sampling of insects and for spraying of agro-chemicals and use of drones for surveying arthropods has also been carried out in recent years (Carrasco-Escobar et al, 2022;Madden et al, 2022;Ryu et al, 2022;BettiSorbelli et al, 2023;Trujillano et al, 2023). In Japan, drones .…”

Section: Discussionmentioning

confidence: 99%

Rapid assessment and management of outbreak of Rice Swarming Caterpillar by drone: a BIG need of the hour

Sarma,

Damgaard,

Saikia

et al. 2024

Front. Sustain. Food Syst.

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Section: Discussionmentioning

confidence: 99%

Rapid assessment and management of outbreak of Rice Swarming Caterpillar by drone: a BIG need of the hour

Sarma,

Damgaard,

Saikia

et al. 2024

Front. Sustain. Food Syst.

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“…In the case of modern orchards, for UAV navigation inside the orchard, among the rows of trees, the orchard can be modeled as an aisle graph ( Sorbelli et al., 2022 ) so that the images are collected as efficiently as possible. In this case, it is about collecting images to detect some harmful insects on trees.…”

Section: Uavs and Cameras Used For Image Acquisition In Orchard Monit...mentioning

confidence: 99%

“…The last option is the most efficient because it can cover a relatively important area in a short time. In ( Sorbelli et al., 2022 ), a method of sweeping individual trees from an orchard for the detection and evaluation of harmful insects (Halyomorpha Halys (HH)) is described. Four NNs were compared ( Ichim et al., 2022 ) to highlight the best-performing network in HH detection.…”

Section: Applicationsmentioning

confidence: 99%

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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“…RGB drone imagery has been used for other vegetation health-related purposes, such as for foliage assessment and pest detection [13] [14]. Drones using RGB cameras have the advantage of being more readily available and cheaper compared to LiDAR cameras.…”

Section: Introductionmentioning

confidence: 99%

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

Yudhistira Kurniawan,

Udin Harun Al Rasyid,

Sukaridhoto

2024

IJCDS

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Mangrove preservation is crucial due to their ecological significance impact. Monitoring the health of mangrove forests is essential for preservation strategy, yet it remains challenging and time-intensive, particularly in remote locations. This study aims to create system to automatically assess mangrove density, providing essential data for informed preservation strategies, such as prioritizing reforestation in low density area. Using drones with RGB cameras to capture aerial imagery, enabling remote data collection. The system utilizes the YOLO neural network object detector to automatically detect objects, enabling quantity estimation. Experiment shows that YOLO object detector is able to detect mangrove tree accurately with 95% recall, 88.3% IoU, and 22ms processing time. The system uses 'tiny' model variant to provide more efficient accuracy compared to computation resource, making it suitable for deployment on computer with limited resources. In comparison to standard model that improves the recall by 4%, IoU by 2%, but demands six times more processing time. Then it calculate the covered area using camera transformation formula. Finally, it calculates the density for mangrove forest health, synchronized with GPS location. With the resulting data on mangrove density, evaluations of mangrove forest health become much easier, facilitating effective preservation actions, such as reforestation in area with low mangrove density.

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A Drone-based Application for Scouting Halyomorpha halys Bugs in Orchards with Multifunctional Nets

Cited by 16 publications

References 5 publications

Rapid assessment and management of outbreak of Rice Swarming Caterpillar by drone: a BIG need of the hour

Rapid assessment and management of outbreak of Rice Swarming Caterpillar by drone: a BIG need of the hour

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

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

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