Author

I. Tryhuba

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Open access Oct 2026

Intelligent Automated Crop Monitoring System Based on Unmanned Aerial Vehicles and Deep Learning for Smart Agriculture

The rapid development of precision agriculture technologies requires intelligent systems capable of automatically analyzing unmanned aerial vehicle (UAV) imagery and transforming image-processing results into structured information for crop-monitoring decision support. This study develops and computationally validates...

A. Tryhuba, Paweł Kiełbasa, N. Koval et al. · Applied Sciences · 0 citations

Open access Sep 2026

Explainable Machine Learning Framework for Forecasting Household Organic Waste Generation to Support Sustainable Bioenergy Systems

The increase in the volume of organic household waste and the transition to a circular economy require the development of reliable forecasting tools capable of ensuring effective planning of waste recycling and bioenergy production systems. Despite significant advances in machine learning methods, most current research...

A. Tryhuba, I. Tryhuba, N. Koval et al. · Energies · 0 citations