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Detection of morphological changes caused by chemical stress in the cyanobacterium Planktothrix agardhii using convolutional neural networks.

Carloto, Ismael; Johnston, Pamela; Pestana, Carlos J.; Lawton, Linda A.


Ismael Carloto


The presence of harmful algal bloom in many reservoirs around the world, alongside the lack of sanitation law/ordinance regarding cyanotoxin monitoring (particularly in developing countries), create a scenario in which the local population could potentially chronically consume cyanotoxin-contaminated waters. Therefore, it is crucial to develop low cost tools to detect possible systems failures and consequent toxin release inferred by morphological changes of cyanobacteria in the raw water. This paper aimed to look for the best combination of convolutional neural network (CNN), optimizer and image segmentation technique to differentiate P. agardhii trichomes before and after chemical stress caused by the addition of hydrogen peroxide. This method takes a step towards accurate monitoring of cyanobacteria in the field without the need for a mobile lab. After testing three different network architectures (AlexNet, 3ConvLayer and 2ConvLayer), four different optimizers (Adam, Adagrad, RMSProp and SDG) and five different image segmentations methods (Canny Edge Detection, Morphological Filter, HP filter, GrabCut and Watershed), the combination 2ConvLayer with Adam optimizer and GrabCut segmentation, provided the highest median accuracy (93.33%) for identifying H2O2-induced morphological changes in P. agardhii. Our results emphasize the fact that the trichome classification problem can be adequately tackled with a limited number of learned features due to the lack of complexity in micrographs from before and after chemical stress. To the authors knowledge, this is the first time that CNNs were applied to detect morphological changes in cyanobacteria caused by chemical stress. Thus, it is a significant step forward in developing low cost tools based on image recognition, to shield water consumers, especially in the poorest regions, against cyanotoxin-contaminated water.


CARLOTO, I., JOHNSTON, P., PESTANA, C.J. and LAWTON, L.A. 2021. Detection of morphological changes caused by chemical stress in the cyanobacterium Planktothrix agardhii using convolutional neural networks. Science of the total environment [online], 784, article 146956. Available from:

Journal Article Type Article
Acceptance Date Mar 31, 2021
Online Publication Date Apr 7, 2021
Publication Date Aug 25, 2021
Deposit Date Apr 26, 2021
Publicly Available Date Apr 26, 2021
Journal Science of the total environment
Print ISSN 0048-9697
Electronic ISSN 1879-1026
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 784
Article Number 146956
Keywords Cyanobacteria; CNN; Water treatment; Hydrogen peroxide; Image recognition; Image segmentation
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