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Chlorophyll

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Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

PloS one
Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral refl...

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Journal of environmental management
Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are ...

Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data.

Environmental research
Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chl...

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

The Science of the total environment
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task ...

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

Environmental science and pollution research international
The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple point...

Comparison of conventional and machine learning regression models for accurate prediction of selected optical active components - A case study: The Gulf of Izmit.

Marine pollution bulletin
This study hypothesizes that advanced machine learning (ML) models can more accurately predict certain critical water quality parameters in marine environments compared to conventional regression techniques. We specifically evaluated the spatio-tempo...

Simultaneously predicting SPAD and water content in rice leaves using hyperspectral imaging with deep multi-task regression and transfer component analysis.

Journal of the science of food and agriculture
BACKGROUND: Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotyp...

Integrating machine learning and remote sensing for long-term monitoring of chlorophyll-a in Chilika Lagoon, India.

Environmental monitoring and assessment
Chlorophyll-a (Chla) is recognized as a key indicator of water quality and ecological health in aquatic ecosystems, offering valuable insights into ecosystem dynamics and changes over time. This study aimed to to develop and validate a robust ML mode...

Algal classification and Chlorophyll-a concentration determination using convolutional neural networks and three-dimensional fluorescence data matrices.

Environmental research
In recent years, the frequency of harmful algal blooms has increased, leading to the release of large quantities of toxins and compounds that cause unpleasant odors and tastes, significantly compromising drinking water quality. Chlorophyll-a (Chl-a) ...

Are more data always better? - Machine learning forecasting of algae based on long-term observations.

Journal of environmental management
Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important...