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Chlorophyll

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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll , Diatoms, Green Algae and Turbidity.

International journal of environmental research and public health
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters ar...

Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.

International journal of environmental research and public health
In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and eco...

Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Molecular omics
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...

Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.

Environmental science and pollution research international
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency...

Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model.

The Science of the total environment
Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and expanding in area. One way to help mitigate their impacts on people's health and livelihoods is to dev...

Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants.

Sensors (Basel, Switzerland)
The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and r...

Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Sensors (Basel, Switzerland)
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. ...

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.

Scientific reports
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problemati...

Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.

Journal of the science of food and agriculture
BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stag...