Environmental monitoring and assessment
Dec 9, 2025
Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll-a (chl-a), a common prox...
Recent advancements in precision agriculture have introduced innovative approaches to addressing plant stress, a critical factor influencing crop productivity and agricultural sustainability. Accurate, real-time prediction of plant stress has become ...
BACKGROUND: Heavy metal contamination in agricultural soils disrupts plant growth and metabolism. Although zinc (Zn) is a necessary element, concentrations above 50 ppm can be toxic to plants. Grafting has emerged as a potential strategy to mitigate ...
Shallow lakes worldwide face escalating pressures from eutrophication and climate change, yet comprehensive monitoring of Chlorophyll-a (Chl-a) spatiotemporal dynamics remains challenging due to the high costs and logistical constraints of traditiona...
Marine phytoplankton are crucial to oceanic ecosystems, yet trends in their activity, monitored through chlorophyll a, remain uncertain due to observational limitations. We generated an ocean chlorophyll a dataset (2001 to 2023) across low to mid-lat...
The global industry of tobacco (Nicotiana tabacum L.) is a profitable one comprising various products, including cigars, cigarettes, chewing tobacco, and smokeless tobacco. The internal quality of the cigarettes is highly related to the chemical comp...
Lakes across the globe are experiencing growing ecological pressure from climate change and human activities. In prairie regions, these pressures often result in shifts in phytoplankton abundance, a key indicator of water quality. Yet identifying the...
Declining water quality poses serious environmental and public health risks, with chlorophyll-a serving as a key biological indicator of harmful algal blooms. This study evaluates the use of a Long Short-Term Memory (LSTM) neural network to forecast ...
The excessive growth of phytoplankton in water can deplete oxygen, release toxins, harm aquatic life, cause economic losses, and threaten coastal residents. Accurately predicting phytoplankton levels is crucial for safeguarding marine life and coasta...
The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has evolved over the years due to increasing technologies, availability of high frequency data, and statistical prowess. Despite the prowess of these data ...
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