# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 6 Conclusions  
This study proposes the design and development of RiceMan, an ontology-based expert system for rice disease identification and control recommendation. As part of our development, we make further progress on improving the two ontologies for rice diseasein Thailand: RiceDO and TreatO. Together with the composition mechanism, these two ontologies lie at the heart of RiceMan's functionalities. RiceMan facilitates users to identify diseases with respect to RiceDO's TBox axioms and suggests controls with respect to TreatO's TBox axioms. Though both ontologies are initially built for RiceMan, they can be potentially extended for other expert systems in the agriculture domain with some revisions to comply with the adapted problems. To ensure its usability and practicability, we evaluate the developed ontologies and the expert system in comprehensive viewpoints with four user groups: (1) ontology specialists; (2) senior agronomists; (3) junior agronomists; and (4) scholars/agricultural students. From the ontology specialists' perspective, RiceDO and TreatO are modeled soundly, appropriately, and consistently; hence, they can support the users to write efficient queries. From the senior agronomists' perspectives, the knowledge related to rice diseases is modeled almost completely, but the knowledge regarding paddy insects may be insufficient. The correctness of the modeled axioms is acceptable, but requires some improvement in terms of the vocabulary. Regarding the usability evaluation, the participants found themselves pleasant with the Thai version, even though they faced some difficulties in the observation data input with the current user interface. These results demonstrate that RiceMan is a promising approach to the rice disease identification and control recommendation problem. There are several future directions. Firstly, RiceDO and TreatO can be enhanced with modeling of axioms from various aspects of information and reliable knowledge sources. Secondly, we aim at collaborating more closely with agronomists at multiple stages of ontology development to clean up some inconsistency that may happen between the adopted knowledge sources and domain experts' tacit knowledge. Besides, we are interested in applying ontology learning techniques to (semi-)automatically model and enhance the constructed knowledge base. Finally, considering potential applications of our ontologies with machine learning for disease identification, we plan to investigate and study the development of latent representation from RiceDO and TreatO. Inspired by [50], each class and property can be trained based on self-supervised learning to find relevant vector representations from the ontologies. In [50], ontologies from BioPortal ([[https://bioportal.bioontology.org/](https://bioportal.bioontology.org/)]([https://bioportal.bioontology.org/](https://bioportal.bioontology.org/)), accessed on 1 November 2021) are used to develop semantic word embedding for applications of monitoring diabetes and blood pressure (BP) patients. In the near future, we plan to attempt similar directions based on our ontologies for monitoring diseases from a multi-modal context. Conceptualization, W.J. and C.A. ; methodology, W.J. ; software, T.R. ; validation, W.J., C.A., T.R. and F.A. ; formal analysis, T.R. ; investigation, W.J. ; resources, W.J. ; data curation, W.J. ; writing--original draft preparation, W.J. and T.R. ; writing--review and editing, W.J., C.A., T.R. and F.A. ; visualization, W.J. ; supervision, C.A. and F.A. ; funding acquisition, F.A. All authors have read and agreed to the published version of the manuscript. This research was funded by National Institute of Informatics, Japan. Not applicable.