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Comparing machine-learning models of different levels of complexity for crop protection: A look into the complexity-accuracy tradeoff

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  • Additional Information
    • Contributors:
      Les instituts techniques agricoles (Acta); Large Scale Collaborative Data Mining (LACODAM); Inria Rennes – Bretagne Atlantique; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7); Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA); Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT); AGroécologie, Innovations, teRritoires (AGIR); Institut National Polytechnique (Toulouse) (Toulouse INP); Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN); Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE); Institut Français de la Vigne et du Vin (IFV); Institut Technique de la Betterave (ITB); Confédération Générale des Planteurs de Betteraves; CASDAR grants of the French Ministry of Agriculture : RMT-SDMAA-19WRT034; RegEpi project (ECOPHYTO R&D program, Ecophyto-2019-REGEPI grant, French Biodiversity Agency - OFB); French applied agricultural research organization for sugar beet (ITB-Institut Technique de la Betterave); ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016)
    • Publication Information:
      CCSD
      Elsevier
    • Publication Date:
      2024
    • Collection:
      Institut National de la Recherche Agronomique: ProdINRA
    • Abstract:
      International audience ; Crop diseases and pests constitute significant causes of yield losses for crops. To limit the harm incurred by those events, farmers resort to plant protection products. Such products are known to have adverse effects both on the environment and on human health. Agronomists make continuous efforts to limit the usage of plant protection products to situations where those products are strictly necessary. To determine such situations, agronomists and policy-makers often rely on decision support tools to model and predict the dynamics of plant diseases. Decision support tools are based either on mechanistic models or on statistical approaches learned from large datasets of biotic (e.g., disease incidence, plant phenological stage) and abiotic (meteorological, soil characteristics) observations in cultures. The surge of powerful machine learning (ML) methods in the last decade makes such approaches a natural pathway to model the dynamics of plant diseases. Machine learning models can reveal the factors that contribute the most to disease and pests outbreaks, provided that those models are simple enough for human inspection. Simplicity, however, may come at the price of lower prediction performances when compared to more complex models. In this paper, we offer a deep look at the performance of ML models of different complexity when used on two use cases of crop disease prediction: downy mildew in the grapevine, and Cercospora leaf spot in the sugar beet. We compare model accuracy and complexity using a year-based cross-validation approach. Our results suggest that interannual meteorological variations are a very important factor in plant disease prediction. Moreover, in line with the observations of the research community in interpretable ML, model complexity stands in clear trade-off with accuracy. This makes models of intermediate complexity appealing for predicting the dynamics of crop diseases as they can provide explicit insights about the rationale of their predictions.
    • Relation:
      WOS: 001133848300001
    • Accession Number:
      10.1016/j.atech.2023.100380
    • Online Access:
      https://hal.science/hal-04382202
      https://hal.science/hal-04382202v1/document
      https://hal.science/hal-04382202v1/file/20230828_Gauriau-2023_Comparing%20machine-learning%20models%20of%20different%20levels%20of%20complexity.pdf
      https://doi.org/10.1016/j.atech.2023.100380
    • Rights:
      http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.DF0C592A