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Nikos Tziolas - University of Florida. Gainesville, FL, US

Nikos Tziolas

Assistant Professor | University of Florida

Gainesville, FL, UNITED STATES

Nikos Tziolas develops interpretable AI techniques and integrated sensing systems to support informed decision-making in agriculture.


Nikos Tziolas' research mission is to integrate innovative computing techniques, based on explainable artificial intelligence, into agricultural science to improve process understanding and attribution of events with strong impacts on soil health and crop productivity. With his team, the main research is focused on investigating multidimensional and integrated sensing approaches along with artificial intelligence techniques to improve capacities for soil health monitoring. Nikos' ambition is to pave the way for the development of evidence-based conservation recommendations for policies and sustainable services for relevant economic operators.

Areas of Expertise (8)

Soil Science

Deep Learning



Artificial Intelligence

Earth Observation

Big Data Analytics


Media Appearances (1)

From hoops star to AI scientist: UF/IFAS researcher hopes to help Florida farmers protect their soil

Morning Ag Clips  online


Basketball took center stage for Nikos Tziolas as he grew up in Greece. It shaped him into a team player, a trait he took into his career as an agricultural engineer. Even though his hoops glory days are behind him, you can still find Tziolas on the basketball courts, relishing the chance to play with students at the UF/IFAS Southwest Florida Research and Education Center, where he’s an assistant professor of soil, water, and ecosystem sciences at SWFREC.

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Articles (3)

Estimation of water-infiltration rate in Mediterranean sandy soils using airborne hyperspectral sensors


Nicolas Francos, et. al


The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure.

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In situ grape ripeness estimation via hyperspectral imaging and deep autoencoders

Computers and Electronics in Agriculture

Nikolaos L. Tsakiridis, et. al


The estimation of the grapes’ maturity in the field using non-destructive techniques is of high interest for the high-valued vinified grapes, particularly towards the development of fully automated agrobots that perform selective harvesting operations. Whereas infrared spectroscopy has been employed using point spectrometers in the laboratory and in the field, imaging spectrometers have mainly been tested in controlled laboratory conditions due to issues with varying illumination.

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On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries

Remote Sensing

Konstantinos Karyotis, et. al


In-situ infrared soil spectroscopy is prone to the effects of ambient factors, such as moisture, shadows, or roughness, resulting in measurements of compromised quality, which is amplified when multiple sensors are used for data collection. Aiming to provide accurate estimations of common physicochemical soil properties, such as soil organic carbon (SOC), texture, pH, and calcium carbonates based on in-situ reflectance captured by a set of low-cost spectrometers operating at the shortwave infrared region...

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