Study on Holographic Image Recognition Technology of Zooplankton

Shi, Zhengrui; Wang, Kai; Cao, Lujie; Ren, Yu; Han, Yu; Ma, Shenglin

doi:10.12783/dtcse/cisnrc2019/33361

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…at six different measurement locations. For zooplankton holograms, Shi et al (2019) applied YOLOv2 (Redmon and Farhadi 2017) architecture to categorize them into four categories based on their distinct morphological features. However, their approach only demonstrates good performance (> 94% precision rate) for planktons close to the focal plane of holograms, with performance deteriorating rapidly for planktons locating farther away from the focal plane, significantly limiting the depth of field of their specie classification.…”

Section: Figurementioning

confidence: 99%

Automated plankton classification from holographic imagery with deep convolutional neural networks

Guo

Nyman

Nayak

et al. 2020

Limnology & Ocean Methods

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In situ digital inline holography is a technique which can be used to acquire high‐resolution imagery of plankton and examine their spatial and temporal distributions within the water column in a nonintrusive manner. However, for effective expert identification of an organism from digital holographic imagery, it is necessary to apply a computationally expensive numerical reconstruction algorithm. This lengthy process inhibits real‐time monitoring of plankton distributions. Deep learning methods, such as convolutional neural networks, applied to interference patterns of different organisms from minimally processed holograms can eliminate the need for reconstruction and accomplish real‐time computation. In this article, we integrate deep learning methods with digital inline holography to create a rapid and accurate plankton classification network for 10 classes of organisms that are commonly seen in our data sets. We describe the procedure from preprocessing to classification. Our network achieves 93.8% accuracy when applied to a manually classified testing data set. Upon further application of a probability filter to eliminate false classification, the average precision and recall are 96.8% and 95.0%, respectively. Furthermore, the network was applied to 7500 in situ holograms collected at East Sound in Washington during a vertical profile to characterize depth distribution of the local diatoms. The results are in agreement with simultaneously recorded independent chlorophyll concentration depth profiles. This lightweight network exemplifies its capability for real‐time, high‐accuracy plankton classification and it has the potential to be deployed on imaging instruments for long‐term in situ plankton monitoring.

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Section: Figurementioning

confidence: 99%

Automated plankton classification from holographic imagery with deep convolutional neural networks

Guo

Nyman

Nayak

et al. 2020

Limnology & Ocean Methods

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“… 110 Similarly, the YOLO V2 model performed considerably well (with a precision of 94% and a recall rate of 88%) in classifying ZP from the holographic images with a sharpness assessment score equal to 0.6 or more. 111 This approach demonstrated that the efficacy of CNN could be applied to various plankton and biological imaging classification systems with eventual application in ecological and fisheries management. In combination with SVM with different CNN, models showed increased classification and recall accuracy (7.13% and 6.41%, respectively).…”

Section: Biological Oceanographymentioning

confidence: 96%

Applications of Machine Learning in Chemical and Biological Oceanography

et al. 2023

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Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

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“…For automatic bounding box detection, Convolutional Neural Networks (CNNs) can improve performance in terms of reliability and robustness (Girshick, 2015;Redmon et al, 2016). This has been demonstrated in microscopy image detection, mainly for planktonic organisms (Shi et al, 2019;Li et al, 2021), and also specifically for benthic diatoms (Salido et al, 2020). However, the approaches still suffer from overlapping bounding boxes when diatoms are close to each other.…”

Section: Introductionmentioning

confidence: 99%