Assessment of a Markov logic model of crop rotations for early crop mappingReport as inadecuate




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1 CESBIO - Centre d-études spatiales de la biosphère

Abstract : Detailed and timely information on crop area, production and yield isimportant for the assessment of environmental impacts of agriculture,for the monitoring of the land use and management practices, and forfood security early warning systems. A machine learningapproach is proposed to model crop rotations which can predict with good accuracy,at the beginning of the agricultural season, the crops most likely tobe present in a given field using the crop sequence of the previous 3to 5 years. The approach is able to learn from data and to integrateexpert knowledge represented as first-order logic rules. Itsaccuracy is assessed using the French Land Parcel Information System implementedin the frame of the EU-s Common Agricultural Policy. This assessmentis done using different settings in terms of temporal depth andspatial generalization coverage. The obtained results show that theproposed 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 currentapproaches based on remote sensing imagery.

Keywords : crop rotations early crop type mapping Markov Logic Networks





Author: Julien Osman - Jordi Inglada - Jean-François Dejoux -

Source: https://hal.archives-ouvertes.fr/



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