Deep learning algorithm as a strategy for detection an invasive species in uncontrolled environment

Martínez-González, Ángel Trinidad; Ramírez-Rivera, Víctor Manuel; Caballero-Vázquez, José Adán; Jáuregui, David Antonio Gómez

doi:10.1007/s11160-021-09667-7

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Recently, several studies attempted to detect invasive alien species in the wild with deep learning. Examples include detecting Anolis lizards (Aota et al 2021), lionfish (Martínez-González et al 2021), and Asian black hornets (O'Shea-Wheller et al 2024) from images, and barred owls (Kelly et al 2023) and bullfrogs (Bota et al 2024) from sounds. These studies suggest that a deep learning model can detect invasive alien species when local training samples (i.e., samples collected at sites of actual application) are available.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based detector of invasive alien frogs,Polypedates leucomystax and Rhinella marina, on an island at invasion front

Kimura,

Fukuyama,

Fukuyama

2024

Preprint

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This study aimed to develop and evaluate a deep learning-based detector for Southeast Asian treefrog (Polypedates leucomystax) and cane toad (Rhinella marina) on Iriomote Island, a Natural World Heritage Site located 30 km from established populations of these alien species on the nearby Ishigaki Island. Although a deep learning model typically requires local training data to be most accurate, the alien frogs have been eradicated from Iriomote Island, making such data unavailable. To address the data gap, we first trained the BirdNET model with acoustic data collected on Ishigaki Island, where these species were common, as well as native frog calls from Iriomote Island. Next, we evaluated model performance using (1) wild sounds on Ishigaki Island, and (2) sounds obtained by playing back the calls of the alien species on Iriomote Island. Model precision and recall for wild sounds were both 0.987 for P. leucomystax and 1 for R. marina. For playback sounds, model recall values decreased (0.629 for P. leucomystax and 0.906 for R. marina), while precisions remained nearly identical (1 for both P. leucomystax and R. marina). Despite the lower recall particularly for P. leucomystax, playback survey dates were mostly identifiable from the high number of detections. These results suggest that data from Ishigaki Island enabled training a model with adequate, though not complete, generalization across invasion front.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based detector of invasive alien frogs,Polypedates leucomystax and Rhinella marina, on an island at invasion front

Kimura,

Fukuyama,

Fukuyama

2024

Preprint

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OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

2024

Multimed Tools Appl

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The use of stereo cameras and artificial intelligence data to support capture fisheries landing data monitoring

Wibowo¹,

Hargiyatno²,

Faizah³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Reliable data and data collection are important key factors in realizing sustainable fisheries management. Landing data collected by the fishing port authority through has issues with coverage and accuracy. The fast development of image processing and artificial intelligence (AI) analysis opens the possibilities of automatic catch monitoring and data collection in the fishing port. This paper aims to develop a catch data collection program in fishing ports based on stereo camera video monitoring which AI processes. The first version of the devices, FishQi-L (Fish Quantitative Intelligence in Landing site), was developed. FishQi-L consisted of a set stereo camera and a pre-programed JETSON Nano image/video processor. The stereo camera allows image size quantification, size estimation, and spatial mapping capabilities. FishQi-L was able to detect the character and dimensions of objects, the trial object in Pekalongan was “Basket” with mean average precision (mAP) of the detection was 84% and 0.4387 losses. To increase the accuracy and reliability of the systems, we need to increase the number of data and data training iterations.

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Deep learning algorithm as a strategy for detection an invasive species in uncontrolled environment

Cited by 4 publications

References 41 publications

Deep learning-based detector of invasive alien frogs,Polypedates leucomystax and Rhinella marina, on an island at invasion front

Deep learning-based detector of invasive alien frogs,Polypedates leucomystax and Rhinella marina, on an island at invasion front

OMSACC-SGRAN: an implementation of hybrid Optimized Multi-Scale Atrous Convoluted CNN with Self Guided Residual Attention Network for fish species classification

The use of stereo cameras and artificial intelligence data to support capture fisheries landing data monitoring

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