Assessment of a Markov logic model of crop rotations for early crop mapping
Résumé
Detailed and timely information on crop area, production and yield is
important for the assessment of environmental impacts of agriculture,
for the monitoring of the land use and management practices, and for
food security early warning systems. A machine learning
approach is proposed to model crop rotations which can predict with good accuracy,
at the beginning of the agricultural season, the crops most likely to
be present in a given field using the crop sequence of the previous 3
to 5 years. The approach is able to learn from data and to integrate
expert knowledge represented as first-order logic rules. Its
accuracy is assessed using the French Land Parcel Information System implemented
in the frame of the EU's Common Agricultural Policy. This assessment
is done using different settings in terms of temporal depth and
spatial generalization coverage. The obtained results show that the
proposed approach is able to predict the crop type of each field,
before the beginning of the crop season, with an accuracy as high as 60\%, which is better than the results obtained with current
approaches based on remote sensing imagery.
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