[SimLearn]
[SimLearn]
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The SimLearn project uses semantic information processing and machine learning to combine process-model simulation data with real-world farm data in order to to support decision making in agriculture.
The SimLearn project uses semantic information processing and machine learning to combine process-model simulation data with real-world farm data in order to to support decision making in agriculture.
Reference: Muenzberg et al. 2022
[FASAL]
[FASAL]
Forecasting Agricultural Output Using Space, Agrometeorology and Land Based Observations (FASAL)
Forecasting Agricultural Output Using Space, Agrometeorology and Land Based Observations (FASAL)
Objectives:The major goal of the project is to develop, validate and issue multiple crop yield forecasts for the major crops namely Rice (aman & boro season), Potato and Mustard in the Red and Laterite Zone of West Bengal at initial stage (F1), mid season (F2) and pre-harvest stage (F3).
Objectives:The major goal of the project is to develop, validate and issue multiple crop yield forecasts for the major crops namely Rice (aman & boro season), Potato and Mustard in the Red and Laterite Zone of West Bengal at initial stage (F1), mid season (F2) and pre-harvest stage (F3).
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