Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Khruschev, S. S.; Plyusnina, Т. Yu.; Antal, Taras K.; Pogosyan, S. I.; Riznichenko, G. Yu.; Rubin, Andrey B.

doi:10.1007/s12551-022-00982-2

Cited by 10 publications

(4 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remote sensing techniques, combined with artificial intelligence models, have revolutionized the way scientists study, and manage the Earth's natural resources [19,20]. These techniques involve the use of sensors to collect data remotely, often from satellites, aircraft or drones [21][22][23]. By using artificial intelligence algorithms to analyze the collected data, researchers can gain insights into environmental patterns and make predictions about future trends [24,25].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Rodríguez-López,

Usta,

Duran-Llacer

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)2), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Rodríguez-López,

Usta,

Duran-Llacer

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In terms of data analysis, a wide range of machine-learning (ML) techniques may be exploited to establish connection between optical spectroscopy data and the activity of the photosynthetic apparatus of plants and phytoplankton [179]. ML represents a ubiquitous tool that can provide new methods of data analysis [180].…”

Section: Future Directionsmentioning

confidence: 99%

High-Resolution Frequency-Domain Spectroscopic and Modeling Studies of Photosystem I (PSI), PSI Mutants and PSI Supercomplexes

Zazubovich,

Jankowiak

2024

IJMS

View full text Add to dashboard Cite

Photosystem I (PSI) is one of the two main pigment–protein complexes where the primary steps of oxygenic photosynthesis take place. This review describes low-temperature frequency-domain experiments (absorption, emission, circular dichroism, resonant and non-resonant hole-burned spectra) and modeling efforts reported for PSI in recent years. In particular, we focus on the spectral hole-burning studies, which are not as common in photosynthesis research as the time-domain spectroscopies. Experimental and modeling data obtained for trimeric cyanobacterial Photosystem I (PSI3), PSI3 mutants, and PSI3–IsiA18 supercomplexes are analyzed to provide a more comprehensive understanding of their excitonic structure and excitation energy transfer (EET) processes. Detailed information on the excitonic structure of photosynthetic complexes is essential to determine the structure–function relationship. We will focus on the so-called “red antenna states” of cyanobacterial PSI, as these states play an important role in photochemical processes and EET pathways. The high-resolution data and modeling studies presented here provide additional information on the energetics of the lowest energy states and their chlorophyll (Chl) compositions, as well as the EET pathways and how they are altered by mutations. We present evidence that the low-energy traps observed in PSI are excitonically coupled states with significant charge-transfer (CT) character. The analysis presented for various optical spectra of PSI3 and PSI3-IsiA18 supercomplexes allowed us to make inferences about EET from the IsiA18 ring to the PSI3 core and demonstrate that the number of entry points varies between sample preparations studied by different groups. In our most recent samples, there most likely are three entry points for EET from the IsiA18 ring per the PSI core monomer, with two of these entry points likely being located next to each other. Therefore, there are nine entry points from the IsiA18 ring to the PSI3 trimer. We anticipate that the data discussed below will stimulate further research in this area, providing even more insight into the structure-based models of these important cyanobacterial photosystems.

show abstract

“…Information sources for assessing(Laisk et al, 2002). ML methods are being considered for determining functional parameters of photosynthesis based on local and distant optical assessments, including classical and regression methods, analysis techniques of unsupervised cluster, methods of classi cation, and arti cial neural networks(Khruschev et al, 2022). been noticed that ML techniques have learned and represented real-world problem features as a nested hierarchy of concepts for achieving exemplary performance and exibility (PiconRuiz et al, 2020).…”

mentioning

confidence: 99%

“…The activity of photosynthesis of natural arti cial biocenosis should be monitored as it is crucial for life on Earth(Khruschev et al, 2022). Decreased production of photosynthesis due to anthropogenic in uences might have irreversible damage.…”

mentioning

confidence: 99%

Advancements in Anomaly Detection: A Review of Machine Learning Applications in Cyber-Physical System Networks

Ahmed

2024

Preprint

View full text Add to dashboard Cite

In the practical world, Cyber-Physical Systems have integrated physical systems and software management in the cyber-world, with networks responsible for information interchange. CPSs are key technologies for various industrial domains, including intelligent medical systems, transport systems, and smart grids. The advancements in cybersecurity have surpassed the rapid growth of CPS, with new security challenges and threat models that lack an integrated and cohesive framework. The review methodology includes the search strategy along with the inclusion and exclusion criteria of fifteen studies conducted in the past ten years. The studies specific to the relevant topic have been added, while the others have been excluded. According to the results, Machine Learning (ML) algorithms and systems can synthesize data. It is employed in cyber-physical security to alleviate concerns regarding the safety and reliability of the findings. ML offers a solution to complex problems, enhancing computer-human interaction and enabling problem-solving in areas where custom-built algorithms are impractical. A comprehensive overview of the application of ML across various domains, such as smart grids, smart vehicles, healthcare systems, and environmental monitoring, has been included. However, a few challenges are associated with implementing ML techniques in CPS networks, including feature selection complexity, model performance, deployment challenges, algorithm biases, model mismatches, and the need to foster a robust safety culture. Overall, integrating ML techniques with CPS networks holds promise for enhancing system safety, reliability, and security but requires ongoing refinement and adaptation to address existing limitations and emerging threats.

show abstract

Machine learning methods for assessing photosynthetic activity: environmental monitoring applications

Cited by 10 publications

References 99 publications

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

Estimation of Water Quality Parameters through a Combination of Deep Learning and Remote Sensing Techniques in a Lake in Southern Chile

High-Resolution Frequency-Domain Spectroscopic and Modeling Studies of Photosystem I (PSI), PSI Mutants and PSI Supercomplexes

Advancements in Anomaly Detection: A Review of Machine Learning Applications in Cyber-Physical System Networks

Contact Info

Product

Resources

About