FramePests: A Comprehensive Framework for Crop Pests Modeling and Forecasting

Autor: Jacques Avelino, Natacha Motisi, Emmanuel Lasso, Juan Carlos Corrales
Přispěvatelé: Universidad del Cauca [Popayán], Plant Health Institute of Montpellier (UMR PHIM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de Montpellier (UM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Doctoral Program of Telematics Engineering and the Telematics Engineering Group (GIT) of the University of Cauca, Programa Centroamericano de Gestión Integral de la Roya del Café (PROCAGICA)’’ through the EU under Grant DCI-ALA/2015/365-17, Tropical Agricultural Research and Higher Education Center (CATIE)., InnovAccion Cauca Project of the Colombian Science, Technology and Innovation Fund (SGR-CTI), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
02 engineering and technology
computer.software_genre
Data modeling
Surveillance des déprédateurs
Predictive models
knowledge-based model
0202 electrical engineering
electronic engineering
information engineering
General Materials Science
Analytical models
computer.programming_language
2. Zero hunger
Food security
Ravageur des plantes
Crop pest forecasting
data-based model
General Engineering
Data models
Agriculture
technique de prévision
Système basé sur la connaissance
Systematic review
Knowledge base
020201 artificial intelligence & image processing
Knowledge based systems
Electrical engineering. Electronics. Nuclear engineering
Rust (programming language)
General Computer Science
020209 energy
Machine learning
Knowledge-based systems
smart farming
Crop pest
Systematics
business.industry
Modèle de simulation
H10 - Ravageurs des plantes
TK1-9971
[SDV.BV.PEP]Life Sciences [q-bio]/Vegetal Biology/Phytopathology and phytopharmacy
Modélisation
Artificial intelligence
State (computer science)
Gradient boosting
business
computer
Forecasting
Zdroj: IEEE Access
IEEE Access, 2021, 9, pp.115579-115598. ⟨10.1109/ACCESS.2021.3104537⟩
IEEE Access, Vol 9, Pp 115579-115598 (2021)
IEEE Access, IEEE, 2021, 9, pp.115579-115598. ⟨10.1109/ACCESS.2021.3104537⟩
ISSN: 2169-3536
Popis: Crop pests are among the greatest threats to food security, generating broad economic, social, and environmental impacts. These pests interact with their hosts and the environment through complex pathways, and it is increasingly common to find professionals from different areas gathering into projects that attempt to deal with this complexity. We propose a framework called FramePests guiding steps and activities for crop pest modeling and forecasting. From theoretical references about carrying out mappings and systematic reviews of the literature, the framework proposes a series of steps leading to a state of science as a knowledge base for modeling tasks. Then, two modeling solutions, based on data and knowledge are used. Finally, the model outputs and performances are compared. The application of the proposed framework was demonstrated for coffee leaf rust modeling, for which we obtained a data-based model built using a gradient boosting algorithm (XGBoost) with a mean absolute error of 7.19% and a knowledge-based model represented by a hierarchical multi-criteria decision structure with an accuracy of 56.03%. A complementary study for our case study allowed us to explore how elements of a data-based model can improve a knowledge-based model, improving its accuracy by 7.07%. and showed that knowledge-based modeling can be an alternative to data-based modeling when the available dataset has approximately 60 instances. Data-based models tend to have better performance, but their replicability is conditioned by the diversity in the dataset used. Knowledge-based models may be simpler but allow expert supervision, and these models are not usually tied to specific sites.
Databáze: OpenAIRE
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