Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images

• Published in: Water (Basel) Vol. 14; no. 14; p. 2219
• Main Authors: Abdullah, Ali, Sikandar, Khan, Ziaullah, Hussain, Ali, Athar, Ali, Kim, Hee-Cheol
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2022
• Subjects: Accuracy; Algae; algae detection; Algal blooms; Algorithms; Aquatic ecosystems; Aquatic life; Artificial intelligence; Classification; Computer vision; Cyanobacteria; Datasets; Deep learning; Drinking water; Eutrophication; Fresh water; HAB; Health aspects; Health risks; Image classification; Information management; Inspection; Machine learning; Machine vision; Microscopes; microscopic image; Neural networks; object detection; Reservoirs; Species classification; Support vector machines; System effectiveness; Underwater pipelines; Water quality; YOLO model; South Korea
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w14142219

The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of pure and freshwater. It increases the risk to human health, water bodies and overall aquatic ecosystem. It is necessary to continuously monitor and perform proper action against HAB. The inspection of algae blooms by using conventional methods, like algae detection under microscopes, is a difficult, expensive, and time-consuming task, however, computer vision-based deep learning models play a vital role in identifying and detecting harmful algae growth in aquatic ecosystems and water reservoirs. Many studies have been conducted to address harmful algae growth by using a CNN based model, however, the YOLO model is considered more accurate in identifying the algae. This advanced deep learning method is extensively used to detect algae and classify them according to their corresponding category. In this study, we used various versions of the convolution neural network (CNN) based on the You Only Look Once (YOLO) model. Recently YOLOv5 has been getting more attention due to its performance in real-time object detection. We performed a series of experiments on our custom microscopic images dataset by using YOLOv3, YOLOv4, and YOLOv5 to detect and classify the harmful algae bloom (HAB) of four classes. We used pre-processing techniques to enhance the quantity of data. The mean average precision (mAP) of YOLOv3, YOLOv4, and YOLO v5 is 75.3%, 83.0%, and 91.0% respectively. For the monitoring of algae bloom in freshwater, computer-aided based systems are very helpful and effective. To the best of our knowledge, this work is pioneering in the AI community for applying the YOLO models to detect algae and classify from microscopic images.