Detection of Anolis carolinensis using drone images and a deep neural network: an effective tool for controlling invasive species

Aota, Tomoki; Ashizawa, Koh; Mori, Hideaki; Toda, Mikito; Chiba, Satoshi

doi:10.1007/s10530-020-02434-y

Cited by 22 publications

(28 citation statements)

References 18 publications

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“…The results of their experiments showed that for the classification based on convolutional neural networks, the features obtained from the adaptive filter banks followed by time-averaging the squared modulus of the filters' output perform better than the canonical Fourier transform-based mel-spectrogram coefficients. They believed that the alternative adaptive approaches with center frequencies or time-averaging lengths learned from the training data performed equally well [ 14 ]. Liu et al exploited the low-level information from the spectrograms of audio and developed a novel CNN architecture that took the multiscale time-frequency information into consideration.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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“…Finally, it is worth noting that, since individuals were sheltered in most of the detections, the development of novel techniques to detect animals while immobile or sheltered is crucial to improve control success 67,[75][76][77] . Due to visual surveys being extremely time-and resource-consuming 15 for such a secretive snake, increasing detection on surface still requires further technological advances-e.g., remote sensing techniques 78 .…”

Section: Discussionmentioning

confidence: 99%

Spatial ecology to strengthen invasive snake management on islands

Maestresalas

Piquet

López-Darias

2023

Sci Rep

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Knowledge on the spatial ecology of invasive predators positively contributes to optimizing their management, especially when involving cryptic and secretive species, such as snakes. However, this information is lacking for most invasive snakes, particularly on islands, where they are known to cause severe ecological and socio-economic impacts. This research is focused on assessing the spatial ecology of the California kingsnake (Lampropeltis californiae) on Gran Canaria to strengthen management actions. We monitored 15 radio-tagged individuals once per day on 9–11 days per month from July 2020 to June 2021 to calculate the species' home range and describe annual activity patterns in the invaded range. To account for the species' diel activity during the emergence period, we additionally monitored snakes from January to May 2021 during three consecutive days per month in four different time intervals each day. We detected movement (consecutive detections at least 6 m apart) in 31.68% of the 1146 detections during the whole monitoring period. Movements most frequently detected were shorter than 100 m (82.24%), and among them the range 0–20 m was the most recurrent (27.03%). The mean distance of movement was 62.57 ± 62.62 m in 1–2 days. Average home range was 4.27 ± 5.35 ha—calculated with the Autocorrelated Kernel Density Estimator (AKDE) at 95%—and did not significantly vary with SVL nor sex. We detected an extremely low value of motion variance (0.76 ± 2.62 σ2m) compared to other studies, with a general inactivity period from November to February, January being the less active month of the year. Diel activity was higher during central and evening hours than during early morning and night. Our results should be useful to improve control programs for this invasive snake (e.g., trap placement and visual survey guidance) on Gran Canaria. Our research highlights the importance of gathering spatial information on invasive snakes to enhance control actions, which can contribute to the management of secretive invasive snakes worldwide.

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“…Imagery collected by UAVs, camera traps, and other remote automated photographic technology can be combined with machine‐learning and artificial intelligence algorithms to automate monitoring across broad areas (e.g. Aota et al ., 2021).…”

Section: Ias Occurrence Datamentioning

confidence: 99%

A framework to integrate innovations in invasion science for proactive management

et al. 2022

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Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.

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Detection of Anolis carolinensis using drone images and a deep neural network: an effective tool for controlling invasive species

Cited by 22 publications

References 18 publications

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Spatial ecology to strengthen invasive snake management on islands

A framework to integrate innovations in invasion science for proactive management

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