This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Trends in digital image processing of isolated microalgae by incorporating classification algorithm.

Algae are widely distributed and have a considerable impact on water quality. Harmful algae can degrade water quality and be detrimental to aquaculture, while beneficial algae are widely used. The accuracy and speed of existing intelligent algae detection methods are available, but the size of parameters of models is large, the equipment requirements are high, the deployment costs are high, and there is still little research on lightweight detection methods in the area of algae detection. In this paper, we propose an improved Algae-YOLO object detection approach, which is based on ShuffleNetV2 as the YOLO backbone network to reduce the parameter space, adapting the ECA attention mechanism to improve detection accuracy, and redesigning the neck structure replacing the neck structure with ghost convolution module for reducing the size of parameters, finally the method achieved the comparable accuracy. Experiments showed that the Algal-YOLO approach in this paper reduces the size of parameters by 82.3%, and the computation (FLOPs) is decreased from 16G to 2.9G with less loss of accuracy, and mAP by only 0.007 when compared to the original YOLOv5s. With high accuracy, the smaller model size are achieved, which reduces the equipment cost during actual deployment and helps to promote the practical application of algae detection.

/pdf/an-improved-algae-yolo-model-based-on-deep-learning-for-h3j3nh71.pdf

An improved algae-YOLO model based on deep learning for object detection of ocean microalgae considering aquacultural lightweight deployment

In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The characterization of microalgae cultures is essential to guarantee the quality of the biomass, and the objective of this work is to achieve a simple and fast method to address this issue. Data acquisition was performed using FlowCam, a device capable of capturing images of the cells detected in a culture sample, which are used as inputs by the model. The model can distinguish between 6 different genera of microalgae, having been trained with several species of each genus. It was further complemented with a classification threshold to discard unwanted objects while improving the overall accuracy of the model. The model achieved an accuracy of up to 97.27% when classifying a culture. The results demonstrate the effectiveness of the Deep Learning models for the characterization of microalgae cultures, it being a useful tool for the monitoring of microalgae cultures in large-scale production facilities while providing accurate characterization over a wide range of genera.

An artificial intelligence approach for identification of microalgae cultures.

Abstract 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. 

pdf/survey-of-automatic-plankton-image-recognition-challenges-2rj028pb1j.pdf

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

Red tide occurs frequently these years and have become a great threat to marine ecology and human health. Monitoring the abundance of red tide algae is very crucial for forecasting and responding potential red tide outbreak. Now there are lots of imaging techniques can rapidly collect algae images which can be used to estimate the algae concentration by classification and counting, but few technologies are specific to red tide algae. In this study, we construct a high-solution color microscopic image dataset contain nine common species of red tide algae. Based on the dataset, we develop a computer vision- based automated red tide recognition and classification system involving image segmentation, artificial feature extraction and classification based on machine learning algorithm. Image segmentation detect the single algae's boundaries and acquire its bounding rectangular areas as the subimage from the original images, even where several objects stick together. Feature extraction process is applied to extract specific feature vectors in terms of own artificial features including shape, color and texture features. Considering the uncertainty of the rotation of the red tide algae and the possible influence of environmental light, the features both have rotation and brightness invariance. we use three different algorithms including Logistic Regression (LR), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) to construct classifiers to classify algae images based on extracted features. We also adopt the idea of ensemble learning to achieve better performance than a single algorithm.¬ The system achieves over 95% segmentation efficiency in the and 96% classification accuracy in about 200 test images, making it comparable with a trained biologist can achieve by manual method. The study proves the potential of identifying and classifying red tide algae by color microscopic images, which may provide new ideas for monitoring red tide by imaging techniques.

Automated red tide algae recognition by the color microscopic image

In natural aquatic environments, the existence of colored dissolved organic matter (CDOM), suspended particles, and colloids can cause scattering and reflection of light and even emit fluorescence itself. Such interference negatively impacts algal fluorescence, further making it unreliable to measure the algal concentration using three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy. In this study, we proposed a novel algal fluorescence anti-interference network (AFAI-Net) based on a convolutional neural network. The main procedure of this model can be divided into two parts: (1) to quickly determine if there is an interference of CDOM or turbidity in the detected algal samples; (2) to correct the interfered samples and output the fluorescent components of the algae. We trained the model using the 3D-EEMs of pure algal samples (non-interfered) and mixed samples of algae and CDOM or turbidity (interfered); as a result, the well-trained model achieved a total classification accuracy of 96.82%, and the RMSE of CDOM and turbidity removal fitting effects were 0.2274 and 0.3423, respectively. Compared with the non-negative weighted least squares (NNLS) regression analysis method, using the CNN model for CDOM correction resulted in 13.11%, 0.65%, and 5.69% reductions in the average deviation rate for PD, PG, and CM, respectively. Furthermore, the spectra corrected by the model predicted algal densities that were closer to the true algal densities. This study provides a new way to remove non-algal factors that affect algal fluorescence spectra in water bodies, which is beneficial to monitoring eutrophication and red tide in aquatic systems.

/pdf/rapid-correction-of-turbidity-and-cdom-interference-on-three-1yu7sr1j.pdf

Rapid Correction of Turbidity and CDOM Interference on Three-Dimensional Fluorescence Spectra of Live Algae Based on Deep Learning

A rapid fluorescence approach on differentiation of typical dinoflagellate of East China Sea.

Detecting marine plankton by means of digital holographic microscopy (DHM) has been successfully deployed in recent decades; however, in most previous studies, the identification of the position, shape, and size of plankton has been neglected, which may negate some of the advantages of DHM. Therefore, the procedure of image fusion has been added between the reconstruction of initial holograms and the final identification, which could help present all the images of plankton clearly in a volume of seawater. A new image fusion method called digital holographic microscopy-fully convolutional networks (DHM-FCN) is proposed, which is based on the improved fully convolutional networks (FCN). The DHM-FCN model runs 20 times faster than traditional image fusion methods and suppresses the noise in the holograms. All plankton in a 2 mm thick water body could be clearly represented in the fusion image. The edges of the plankton in the DHM-FCN fusion image are continuous and clear without speckle noise inside. The neural network model, YOLOv4, for plankton identification and localization, was established. A mean average precision (mAP) of 97.69% was obtained for five species, Alexandrium tamarense, Chattonella marina, Mesodinium rubrum, Scrippsiella trochoidea, and Prorocentrum lima. The results of this study could provide a fast image fusion method and a visual method to detect organisms in water.

/pdf/a-plankton-detection-method-based-on-neural-networks-and-1th1j61i.pdf

A Plankton Detection Method Based on Neural Networks and Digital Holographic Imaging

Image fusion combines image data on the same target collected from multi-source channels, which are processed to extract the most favorable information from each channel and finally synthesize a high-quality image. In this study, image fusion is used to aggregate multi-focus plankton into a single-fused image. An unsupervised image weighted fusion method based on fully convolutional networks (FCN) is proposed, which can clearly and intuitively show the presence of plankton in the detected water body. The fused image has a high contrast, continuous and clear plankton edges, and little information loss, thereby laying a foundation for the next phytoplankton identification.

Image Fusion Method for Improving the Accuracy of Ocean Plankton Recognition

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