A fuzzy logic based soil chemical quality index for cacao
2020
Mora-Herrera, Denys Yohana | Guillaume, Serge | Snoeck, Didier | Zúñiga Escobar, Orlando | Universidad del Valle [Cali] (Univalle) | Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP) ; 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) | Performance des systèmes de culture des plantes pérennes (UPR Système de pérennes) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
International audience
Show more [+] Less [-]English. A soil chemical quality index is designed for cacao production systems. It is based on worldwide scientific knowledge and built using data from three municipalities of Tolima department in Colombia. Fuzzy logic is used in a multicriteria decision making framework in two different ways. First, fuzzy sets are used to model an expert preference relation for each of the individual information sources to turn raw data into satisfaction degrees. Second, fuzzy rules are used to model the interaction between sources to aggregate the individual degrees into a global score. The whole framework is implemented as an open source software called GeoFIS. A part of the data was used for calibration, then the remaining data served as a validation set. The results were easy to analyze and in agreement with field observations. The output inferred by the fuzzy inference system was used as a target to learn the weights of classical numerical aggregation operators. Only the Choquet Integral proved to have a similar modeling ability, but its optimal tuning would have been difficult without learning.
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