Journal Article

Comparative assessment of artificial intelligence (AI)-based algorithms for detection of harmful bloom-forming algae: an eco-environmental approach toward sustainability

Gaur, Ashish, Pant, Gaurav, Jalal, Anand Singh

2023

Overview

Organic effluent enrichment in water may selectively promote algal growth, resulting in water pollution and posing a threat to the aquatic ecosystem. Recent harmful algal blooms (HABs) incidents have highlighted information gaps that still exist, as well as the heightened need for early detection technology developments. Although previous research has demonstrated the importance of deep learning in the identification of algal genera, it is still a challenge to identify or to develop the best-suited convolution neural network (CNN) model for effective monitoring of bloom-forming algae. In the present study, efficiency of deep learning models (MobileNet V-2, Visual Geometry Group-16 (VGG-16), AlexNet, and ResNeXt-50) have been evaluated for the classification of 15 bloom-forming algae. To obtain a high level of accuracy, different convolution layers with adaptive moment estimation (Adam), root-mean-square propagation (RMSprop) as optimizers with softmax and rectified linear unit (ReLU) as activation factors have been used. The classification accuracies of 40, 96, 98, and 99% have been achieved for MobileNet V-2, VGG-16, AlexNet, and ResNeXt-50 model, respectively. We believe that the ResNeXt-50 has the potential to identify algae in a variety of situations with high accuracy and in real time, regardless of the underlying hardware. Such studies pave the path for future AI-based cleaner technologies associated with phycological studies for a sustainable future.

• Publisher: Springer International Publishing,Springer Nature B.V,SpringerOpen
• Subject: Accuracy / Algae / Algal blooms / Algal growth / Algorithms / Aquatic ecosystems / Aquatic Pollution / Artificial intelligence / Artificial neural networks / Classification / Cleaner technologies / Comparative Law / Convolution / Convolution layers / Convolution neural network (CNN) / Deep learning / Deep learning models / Detection / Earth and Environmental Science / Earth Sciences / Eutrophication / Genera / Harmful algal blooms (HABs) / Hydrogeology / Industrial and Production Engineering / International & Foreign Law / Machine learning / Nanotechnology / Neural networks / Original Article / Private International Law / Sustainability / Visual discrimination learning / Waste Water Technology / Water Industry/Water Technologies / Water Management / Water pollution / Water Pollution Control / Water quality