An Efficient Self-Organized Detection System for Algae

Gong, Xingrui; Ma, Chao; Sun, Bai-Wang; Zhang, Junyi

doi:10.3390/s23031609

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…provided a review of various techniques for microalgae classification, while (Yang et al,2021) evaluated different CNN models for harmful versus harmless algae classification, achieving an accuracy of 94.8%. (Gong et al,2023) compared various YOLO architectures for the detection of 54 genera of algae, reporting a Mean Average Precision (MAP) score of 50.5%. (Khan et al,2022) detected harmful algal blooms with VGG-16, AlexNet, GoogleNet, and ResNet-18 CNN architectures, achieving an accuracy of 97.10%.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Classification of Desmids using Transfer Learning

Pardeshi

Deshmukh

2023

JAETS

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This research paper presents a novel approach to classifying microscopic images of desmids using transfer learning and convolutional neural networks (CNNs). The purpose of this study was to automate the tedious task of manually classifying microscopic algae and improve our understanding of water quality in aquatic ecosystems. To accomplish this, we utilized transfer learning to fine-tune 13 pre-trained CNN models on a dataset of five categories of desmids. We evaluated the performance of our models using several metrics, including accuracy, precision, recall, and F1-score. Our results show that transfer learning can significantly improve the classification accuracy of microscopic images of desmids, and efficient CNN models can further enhance performance. The practical implications of this research include a more efficient and accurate method for classifying microscopic algae and assessing water quality. The theoretical implications include a better understanding of the application of transfer learning and CNNs in image classification. This research contributes to both theory and practice by providing a new method for automating the classification of microscopic algae and improving our understanding of aquatic ecosystems

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Classification of Desmids using Transfer Learning

Pardeshi

Deshmukh

2023

JAETS

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“…10 The study introduces a novel approach leveraging the YOLOv8 architecture for feature extraction from small objects, thereby establishing a real-time detection system. Such models can be employed in embedded microalgae detection systems, as proposed in the article, 11 capable of scanning and detecting specimens present on a slide within a 5-minute interval, a task that an expert would take 3 hours to perform.…”

Section: Introductionmentioning

confidence: 99%

Microscopic image quality in few-shot GAN-generated cyanobacteria images and its impact on classification networks

Bueno,

Sanchez,

Perona

et al. 2024

Optics, Photonics, and Digital Technologies for Imaging Applications VIII

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Obtaining high-quality images for training AI models in the field of plankton identification, particularly cyanobacteria, is a challenging and time-critical task that necessitates the expertise of biologists. Data augmentation techniques, including conventional methods and GANs, can improve model performance, but GANs typically require large training datasets to produce high-quality results. To tackle this issue, we employed the StyleGAN2ADA model on a dataset of 9 cyanobacteria genera plus non-cyanobacterial microalgae. We evaluated the generated images using both qualitative and quantitative metrics. Qualitative assessments involved a psychophysical test conducted by three expert biologists to identify shape and texture deviations or chromatic aberration that might impede visual classification. Additionally, three non-reference image quality metrics based on perceptual features were used for quantitative assessment. Images meeting quality standards will be incorporated into classification models to assess the performance improvement compared to the original dataset. This comprehensive evaluation process ensured the suitability of generated images for enhancing model performance.

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