Research on Imbalanced Microscopic Image Classification of Harmful Algae

Qiao, Xinqi

doi:10.1109/access.2020.3007646

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The study collected a large-scale colored microscopic algae dataset and applied extensive identification experiments based on the dataset. Lastly, [12] explained a critical development direction for the algae classification and determination based on image analysis of biological-morphological differences. However, this study had to deal with some challenges, such as the high degree of sample imbalance and the difficulty of formalizing the description of local physiological features.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Assisted Expert System for Algae Identification With Certainty Factors

Setiawan¹,

Puspasari

Manik³

et al. 2021

IEEE Access

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Harmful Algal Bloom (HAB) is one of nature's responses to nutrient enrichment in aquatic systems and increasingly occurs in coastal waters, such as in Lampung Bay and Jakarta Bay, Indonesia. HABs present environmental and fisheries management challenges due to their unpredictability, spatial coverage, and detrimental health effects to coastal organisms, including humans. Here, we propose an automated algae species identification system assisted and validated by expert judgment. The system uses ontology as guidance to determine the species of algae and certainty factors to indicate the level of confidence of the experts when providing a statement or judgment for a particular object or event under consideration. We tested the system to identify 60 sample data using 51 predetermined algal characteristics.The tests were narrowed down to the 20 most common HAB-causing algae types found in the study sites and compared with identification by the experts. The results showed that the system has successfully identified the test data with an accuracy of 73.33%. The system also has a high agreement (above 79.75%) with the identification performed by the experts on six algae species. Further improvement on the system's accuracy could facilitate its use as an alternative tool in rapid algal identification or part of an early warning system for HABs.INDEX TERMS Harmful algal bloom, algae identification, ontology, certainty factor, expert system.

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Section: Introductionmentioning

confidence: 99%

Ontology-Assisted Expert System for Algae Identification With Certainty Factors

Setiawan¹,

Puspasari

Manik³

et al. 2021

IEEE Access

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Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives

Eerola,

Batrakhanov,

Barazandeh

et al. 2024

Artif Intell Rev

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Planktonic organisms including phyto-, zoo-, and mixoplankton are key components of aquatic ecosystems and respond quickly to changes in the environment, therefore their monitoring is vital to follow and understand these changes. Advances in imaging technology have enabled novel possibilities to study plankton populations, but the manual classification of images is time consuming and expert-based, making such an approach unsuitable for large-scale application and urging for automatic solutions for the analysis, especially recognizing the plankton species from images. Despite the extensive research done on automatic plankton recognition, the latest cutting-edge methods have not been widely adopted for operational use. In this paper, a comprehensive survey on existing solutions for automatic plankton recognition is presented. First, we identify the most notable challenges that make the development of plankton recognition systems difficult and restrict the deployment of these systems for operational use. Then, we provide a detailed description of solutions found in plankton recognition literature. Finally, we propose a workflow to identify the specific challenges in new datasets and the recommended approaches to address them. Many important challenges remain unsolved including the following: (1) the domain shift between the datasets hindering the development of an imaging instrument independent plankton recognition system, (2) the difficulty to identify and process the images of previously unseen classes and non-plankton particles, and (3) the uncertainty in expert annotations that affects the training of the machine learning models. To build harmonized instrument and location agnostic methods for operational purposes these challenges should be addressed in future research.

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