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Yield forecasting with machine learning and small data: what gains for grains?
[article]
2021
arXiv
pre-print
Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems. Similarly, machine learning methods are increasingly used to process big Earth observation data. However, access to data necessary to train such algorithms is often limited in food-insecure countries. Here, we evaluate the performance of machine learning
arXiv:2104.13246v2
fatcat:2htwmdrpojcf7m5h3gjsehgwo4