# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 2 Related Works
### Existing Ontologies and Vocabularies
Plant Protection Ontology (PPntology) ([[https://sites.google.com/site/ontowords/ontologies](https://sites.google.com/site/ontowords/ontologies)]([https://sites.google.com/site/ontowords/ontologies](https://sites.google.com/site/ontowords/ontologies)), [[https://sites.google.com/site/ppontology/home](https://sites.google.com/site/ppontology/home)]([https://sites.google.com/site/ppontology/home](https://sites.google.com/site/ppontology/home)), accessed on 1 November 2021) [12; 13] classifies barley's disorder into abiotic and biotic. The biotic disorder is also classified further into bacteria, fungi, and viruses. Rice Disease Ontology (RiceDO) and Treatment Ontology (TreatO) [14] initially model rice diseases and insects according to their abnormal characteristics of rice plants and defined relevant biological and chemical controls using ontological classes and relation expressions. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Ontology/Vocabulary**} & \multirow{2}{*}{**Domain of Interest**} & \multicolumn{6}{c}{**Coverage of Plant Related Knowledge**} \\ \cline{3-10}  & & **Pathogen** & **Part** & **Production** & \begin{tabular}{c} Disease \\ Name \\ \end{tabular} & \begin{tabular}{c} Disease \\ Damage \\ \end{tabular} & \begin{tabular}{c} Treatment \\ Name \\ \end{tabular} & \
\begin{tabular}{c} Treatment \\ Usage \\ \end{tabular} &
\begin{tabular}{c} Growth \\ Stage \\ \end{tabular} \\ \hline
**AGROVOC**[1] & Food, nutrition, agriculture, fisheries, animal, environment, forestry, and etc. & & & & & & & & & \\ \hline
**Agriculture Activity** & Agriculture activity & ✘ & \(\odot\) & ✓ & ✘ & ✘ & ✓ & \(\odot\) \\
**Ontology (AAO)**[2] & Agricultural & ✘ & ✓ & ✓ & ✘ & ✔ & ✓ & ✘ & ✓ \\ \hline
**National Agricultural** & Agricultural & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✘ & ✔ \\
**Library** **Measurus** & & & & & & & & & & \\
**(NALT)** & & & & & & & & & & \\
**Trail Ontology (TO)**[5; 6] & Plant trait & ✓ & \(\odot\) & ✘ & ✓ & ✘ & ✘ & ✔ & ✘ \\ \hline
**Phenotype and Trait** & Qualities and & ✘ & ✘ & ✘ & ✘ & \(\odot\) & ✘ & ✘ & ✘ \\
**Ontology (PATO)**[3] & Attributes & ✘ & ✘ & ✘ & ✘ & \(\odot\) & ✘ & ✘ & ✘ \\ \hline
**Plant Ontology** & Plant structures & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✔ \\
**(PO)**[7; 8; 9] &
\begin{tabular}{l} Plant structures \\ and developmental \\ stages \\ \end{tabular} & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✘ & ✔ \\ \hline
**Rice Ontology** & Rice trait & ✓ & ✘ & ✘ & ✔ & ✘ & ✘ & ✘ & ✔ \\
**(CO_320)**[10] &
\begin{tabular}{l} Defining of rice \\ diseases based on \\ the causal agents \\ \end{tabular} & ✘ & ✘ & ✘ & ✔ & ✘ & ✘ & ✘ & ✘ & ✘ \\ \hline
**Plant Protection** & Diagnosis and & ✓ & ✓ & ✘ & ✓ & ✓ & ✓ & ✘ & ✓ \\
**Ontology** & control of & & & & & & & \\
**(PPntology)**[12; 13] & barley disorders & & & & & & & & \\ \hline
**RiceDO and TreatO**[14] & Diseases, insects & ✓ & ✓ & ✓ & \(\odot\) & ✓ & ✓ & ✓ & ✓ \\  & abnormalities, and controls & & & & & & & \\ \hline
**Thai Rice** & Rice variety, & ✓ & ✓ & ✘ & ✘ & ✘ & ✘ & ✘ \\
**Knowledge** & disease, weed & & & & & & & \\
**Ontology**[15] & and pest & & & & & & & & \\ \hline \hline \end{tabular}  
* Remark: ✔ means total coverage, \(\odot\) means partial coverage, and \(\bigstar\) means no coverage.  
\end{table} 
Table 1: Existing, relevant ontologies and vocabularies in the agriculture domain \({}^{\dagger}\). Regarding the engineering discipline of each ontology, CO_320 and PDO exploit annotations for modeling the pathogens and short descriptions of each disease. In contrast, PPOntology models disease names, appearances, and pathogens as individuals in the ontology. While these models can represent the domain knowledge, their choices cause difficulties in employing inference engines, such as reasoning for implicit diseases from a symptom expression. RiceDO and TreatO [14] address this issue by employing a different modeling approach using ontology class definitions. Despite their existence, they have not yet been evaluated and used empirically.