A modeling approach to design a software sensor and analyze agronomical features - Application to sap flow and grape quality relationship
This work addresses the challenge of relating agricultural data to product quality for vineyard management, but it appears incremental as it applies existing methods like functional regression in a specific domain context.
The authors tackled the problem of analyzing complex agronomical features by developing a framework that integrates temporal data, domain knowledge via an ontology, and mathematical models to design a software sensor, applied to French vineyards to explain grape quality (sugar concentration and weight) based on sap flow measurements across vine phenological stages.
This work proposes a framework using temporal data and domain knowledge in order to analyze complex agronomical features. The expertise is first formalized in an ontology, under the form of concepts and relationships between them, and then used in conjunction with raw data and mathematical models to design a software sensor. Next the software sensor outputs are put in relation to product quality, assessed by quantitative measurements. This requires the use of advanced data analysis methods, such as functional regression. The methodology is applied to a case study involving an experimental design in French vineyards. The temporal data consist of sap flow measurements, and the goal is to explain fruit quality (sugar concentration and weight), using vine's water courses through the various vine phenological stages. The results are discussed, as well as the method genericity and robustness.