Assessment of a Markov logic model of crop rotations for early crop mapping

Autor: Julien Osman, Jean-François Dejoux, Jordi Inglada
Přispěvatelé: Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Rok vydání: 2015
Předmět:
Markov Logic Networks
010504 meteorology & atmospheric sciences
Computer science
0211 other engineering and technologies
02 engineering and technology
Agricultural engineering
Horticulture
Logic model
01 natural sciences
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
early crop type mapping
crop rotations
Information system
[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces
environment
021101 geological & geomatics engineering
0105 earth and related environmental sciences
2. Zero hunger
Warning system
Land use
Agroforestry
business.industry
Frame (networking)
Forestry
15. Life on land
Crop rotation
Field (geography)
Computer Science Applications
Agriculture
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Animal Science and Zoology
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
business
Agronomy and Crop Science
Zdroj: Computers and Electronics in Agriculture
Computers and Electronics in Agriculture, Elsevier, 2015, 113, pp.02.015. ⟨10.1016/j.compag.2015.02.015⟩
Computers and Electronics in Agriculture, 2015, 113, pp.02.015. ⟨10.1016/j.compag.2015.02.015⟩
ISSN: 0168-1699
Popis: A prediction model for crop rotations is proposed.The model uses machine learning techniques applied to historic data.It allows the introduction of expert knowledge without re-learning from data.The model is assessed on real data over several years and a large area. 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 3-5years. 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.
Databáze: OpenAIRE
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