AIMC Topic: Soil

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A critical systematic review on spectral-based soil nutrient prediction using machine learning.

Environmental monitoring and assessment Jul 4, 2024
The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quali...
Agriculture Environmental Monitoring Nutrients Machine Learning Soil
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Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation.

The Science of the total environment Jul 3, 2024
In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in ar...
Agriculture Environmental Monitoring Tea Neural Networks, Computer Soil
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New strategy to optimize in-situ fenton oxidation for TPH contaminated soil remediation via artificial neural network approach.

Chemosphere Jul 3, 2024
In-situ remediation of total petroleum hydrocarbon (TPH) contaminated soils via Fenton oxidation is a promising approach. However, determining the proper injection amount of HO and Fe source over the Fenton reaction in the complex geological conditio...
Soil Pollutants Environmental Restoration and Remediation Hydrocarbons Petroleum Hydrogen Peroxide Iron Neural Networks, Computer Oxidation-Reduction Soil
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Optimal biochar selection for cadmium pollution remediation in Chinese agricultural soils via optimized machine learning.

Journal of hazardous materials Jun 27, 2024
Biochar is effective in mitigating heavy metal pollution, and cadmium (Cd) is the primary pollutant in agricultural fields. However, traditional trial-and-error methods for determining the optimal biochar remediation efficiency are time-consuming and...
Agriculture Soil Pollutants Soil China Machine Learning Cadmium Charcoal Environmental Restoration and Remediation
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Predicting the governing factors for the release of colloidal phosphorus using machine learning.

Chemosphere Jun 27, 2024
Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent th...
Iron Colloids Linear Models Machine Learning Phosphorus Soil Environmental Monitoring Soil Pollutants
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Securing China's rice harvest: unveiling dominant factors in production using multi-source data and hybrid machine learning models.

Scientific reports Jun 26, 2024
Ensuring the security of China's rice harvest is imperative for sustainable food production. The existing study addresses a critical need by employing a comprehensive approach that integrates multi-source data, including climate, remote sensing, soil...
Climate Agriculture Oryza Neural Networks, Computer Soil Machine Learning China
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Clivia biosensor: Soil moisture identification based on electrophysiology signals with deep learning.

Biosensors & bioelectronics Jun 25, 2024
Research has shown that plants have the ability to detect environmental changes and generate electrical signals in response. These electrical signals can regulate the physiological state of plants and produce corresponding feedback. This suggests tha...
Neural Networks, Computer Plants Soil Biosensing Techniques Deep Learning Environmental Monitoring Water
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Machine learning in soil nutrient dynamics of alpine grasslands.

The Science of the total environment Jun 25, 2024
As a terrestrial ecosystem, alpine grasslands feature diverse vegetation types and play key roles in regulating water resources and carbon storage, thus shaping global climate. The dynamics of soil nutrients in this ecosystem, responding to regional ...
Phosphorus Climate Change Machine Learning Grassland Soil Nitrogen Ecosystem Nutrients Environmental Monitoring
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Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach.

Chemosphere Jun 24, 2024
The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil sp...
Soil Environmental Monitoring Soil Pollutants Arsenic Bayes Theorem Machine Learning
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Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) prediction model based on limited peat samples using an evolved artificial neural network.

Chemosphere Jun 21, 2024
Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) are involuntary by-products of incomplete combustion and are highly toxic to humans and the environment. The Malaysian peat is often acidic or extremely acidic having high ...
Soil Environmental Monitoring Neural Networks, Computer Algorithms Polychlorinated Dibenzodioxins Dibenzofurans, Polychlorinated Benzofurans Malaysia Soil Pollutants
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