Floods are some of the most devastating natural disasters communities in the United States face, causing billions of dollars ...

For the study, the researchers used a series of machine learning models to analyze and predict potential gully sites based on approximately 200 random gully sites from existing data points within ...

In silico machine learning–enabled detection of polycyclic aromatic hydrocarbons from contaminated soil. Proceedings of the National Academy of Sciences, 2025; 122 (19) DOI: 10.1073/pnas.2427069122 ...

Satellite-based remote sensing, enhanced by machine learning techniques, offers a scalable, cost-effective alternative that enables the identification of contaminants across vast and often ...

• Designed a classification model with 5 classes and 98% testing accuracy using a Convolutional Neural Network. • Applied Data Augmentation and reduced validation losses by 30% by applying MobileNetV2 ...

Soil Health Monitoring through Microbiome-Based Machine Learning: Soil health is critical for maintaining agroecosystems’ ecological and commercial value, requiring the assessment of biological, ...

We present 1-dimensional (1D) convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data. The following CNN models are included: LucasCNN LucasResNet ...

To accomplish this, a prototype was developed capable of predicting the best suitable crop for a specific plot of land based on soil fertility and making recommendations based on weather forecast.

With the increase in information on soil systems, environmental variables and methodological approaches, there is an opportunity to improve the existing soil property and function maps and their ...