  Nowadays, a huge amount of knowledge has been amassed in digital agriculture.
This knowledge and know-how information are collected from various sources,
hence the question is how to organise this knowledge so that it can be
efficiently exploited. Although this knowledge about agriculture practices can
be represented using ontology, rule-based expert systems, or knowledge model
built from data mining processes, the scalability still remains an open issue.
In this study, we propose a knowledge representation model, called an
ontology-based knowledge map, which can collect knowledge from different
sources, store it, and exploit either directly by stakeholders or as an input
to the knowledge discovery process (Data Mining). The proposed model consists
of two stages, 1) build an ontology as a knowledge base for a specific domain
and data mining concepts, and 2) build the ontology-based knowledge map model
for representing and storing the knowledge mined on the crop datasets. A
framework of the proposed model has been implemented in agriculture domain. It
is an efficient and scalable model, and it can be used as knowledge repository
a digital agriculture.
