Assessment of a Markov logic model of crop rotations for early crop mapping
2015
Osman, Julien | Inglada, Jordi | Dejoux, Jean-François | Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
International audience
اظهر المزيد [+] اقل [-]إنجليزي. 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.
اظهر المزيد [+] اقل [-]الكلمات المفتاحية الخاصة بالمكنز الزراعي (أجروفوك)
المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique