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Found 13 papers, 3 papers with code
Next day fire prediction via semantic segmentation
In this paper we present a deep learning pipeline for next day fire prediction.
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.
Causality and Explainability for Trustworthy Integrated Pest Management
Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis.
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach
The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity.
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.
Personalizing Sustainable Agriculture with Causal Machine Learning
To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that.
Pest presence prediction using interpretable machine learning
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint.
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.
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 assessing agricultural land suitability with causal machine learning
Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change.
