The Potato Ontology: Delimitation of the Domain, Modelling Concepts, and Prospects of Performance

Abstract

The ever increasing amount of data gathered by more growers in more years offers possibilities to add value. Therefore-for interested parties and stakeholders-a common and controlled vocabulary of the potato domain that describes concepts, attributes, and the relations between them in a formal way using a standardised knowledge representation language is being developed: a potato ontology. The advantage is that all possible stakeholders will be able to understand the data expressed by this ontology and that software applications can process them automatically. It will also allow the application of advanced numerical techniques that may help to uncover previously unknown correlations. This version of the potato ontology aims at the domain of processing potatoes in a setting of mechanised potato production where growers have access to automated decision support systems and exchange data electronically. This paper describes the procedures to establish such an ontology where competency questions formulated by stakeholders and potential users take a central position. The potato ontology formally describes "Concepts" or "Classes". The three main classes are those used in crop ecology: Crop, Environment and Management. Classes, e.g., biocides are a subclass of agrochemicals, and in turn have a subclass Fungicides. The ontology also describes the "Properties" of classes, e.g., agrochemicals are produced synthetically in a factory; biocides are used to protect crops and fungicides to control fungi. The ontology also describes the "Attributes" (properties) of the concepts, e.g., all agrochemicals have attributes such as dose and time of application and mode of application. "Restrictions" may be that a particular chemical can only be applied with a certain type of equipment, or its application is restricted to a certain period or dose. The ontology also features "Instances" which are the individual data such as a particular herbicide treatment with values for field, time, dose, active ingredient, trademark,

Yield levels of potato crops: Recent achievements and future prospects

“…This desired Performance (P) is determined by Genotype (G), the Environment where the crop is grown (E), its Management (M) and the production conditions Society (S) requires: P = G × E × M × S. Actual crop performance (P) of potato (Haverkort and Top, 2011) can be expressed in dry or fresh matter per unit area or yield of the finished product per unit area recovered from the raw material after processing.…”

On Processing Potato: 1. Survey of the Ontology, History and Participating Actors

“…The potato ontology (Haverkort et al 2005;Haverkort and Top 2011) describes the domain of tuber production, tuber handling and storage of tubers to be processed. An ontology is helpful to organise data in a systematic way to enable interested entities to question the database.…”

Modular structure of web-based decision support systems for integrated pest management. A review

“…Thus, novel approaches to information retrieval using ontologies in the agricultural field may address the limitation of supporting users to find proper information in keyword-based retrieval, by browsing information associated with formal descriptions of the significations of words. This is referred as semantics and the relationships between different objects are made using ontology web language (OWL) (Haverkort and Top 2010;Caracciolo et al 2012).…”

“…Information may include for instance pesticides and chemical ingredients authorized for application, prices of pesticides in relation to company, chemical ingredient and biodegradability, application mode and pest targeting stage, pesticide per harvest period, information concerning life cycle and type of damage, pest morphological characteristics and disease symptoms, etc. (Coulson et al 1989;Haverkort and Top 2010;Damos 2013b). At a second stage, graph-based ranking algorithms can be successfully employed in data summarization, while several query expansion methods (Chakrabarti et al 1999) can resolve information limit on the query based on the prior defined ontologies.…”