15 citations · 42 across the 20 of their papers we have counts for
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Evaluating Digital Agriculture Recommendations with Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou +5
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer hig…
Fuzzy clustering for the within-season estimation of cotton phenology
Vasileios Sitokonstantinou, Alkiviadis Koukos, Ilias Tsoumas +3
Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation,…
Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
Vasileios Sitokonstantinou
Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and…
Towards Global Crop Maps with Transfer Learning
Hyun-Woo Jo, Alkiviadis Koukos, Vasileios Sitokonstantinou +2
The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is nee…
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou +5
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can o…
Personalizing Sustainable Agriculture with Causal Machine Learning
Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla +1
To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, trans…
A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring
Thanassis Drivas, Vasileios Sitokonstantinou, Iason Tsardanidis +3
The modernization of the Common Agricultural Policy (CAP) requires the large scale and frequent monitoring of agricultural land. Towards this direction, the free and open satellite…
Pest presence prediction using interpretable machine learning
Ornela Nanushi, Vasileios Sitokonstantinou, Ilias Tsoumas +1
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the ins…
Towards Space-to-Ground Data Availability for Agriculture Monitoring
George Choumos, Alkiviadis Koukos, Vasileios Sitokonstantinou +1
The recent advances in machine learning and the availability of free and open big Earth data (e.g., Sentinel missions), which cover large areas with high spatial and temporal resol…
Towards assessing agricultural land suitability with causal machine learning
Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla +1
Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate chang…