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Found 7 papers, 3 papers with code
Cloud gap-filling with deep learning for improved grassland monitoring
Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes.
Evaluating Digital Agriculture Recommendations with Causal Inference
To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e. g., yield in this case).
Fuzzy clustering for the within-season estimation of cotton phenology
In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level.
Towards Global Crop Maps with Transfer Learning
A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage.
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators.
A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring
The modernization of the Common Agricultural Policy (CAP) requires the large scale and frequent monitoring of agricultural land.
Towards Space-to-Ground Data Availability for Agriculture Monitoring
Nevertheless, the spatial resolution of satellite images is not always adequate to make accurate decisions for all fields.
