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Found 6 papers, 1 papers with code
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.
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.
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.
