# An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation
## 2 Related Works
### Farming Knowledge Systems and Expert Systems for Plant Disease Diagnosis  
Table 2 presents existing online farming knowledge portals and plant disease diagnosis systems. PlantVillage (www.plantvillage.psu.edu/, accessed on 1 November 2021) [18] is an open-access database, providing knowledge of various crops, enabling the development of plant disease expert systems. Plantwise Knowledge Bank (www.plantwise.org/KnowledgeBank, accessed on 1 November 2021) [19; 20] provides pest diagnostic tools and fact sheets on plants via a website and a mobile application. Evidence in [19] shows that the combination of online fact sheets and plant doctors, to advise farmers, is helpful in practice. [PERSON] et al. [21] reports that 86% of trained plant doctors in China indeed provide comprehensive pest management recommendations, leading to sustainable farming. CROPROTECT ([[https://croprotect.com](https://croprotect.com)]([https://croprotect.com](https://croprotect.com)), accessed on 1 November 2021) [22] is a UK-based website that aims to provide remote farmers with access to knowledge about crops, pests, weeds, and disease management. RiceXpert ([[https://play.google.com/store/apps/details?id=com.icar.ricexpert&hl=en&gl=US](https://play.google.com/store/apps/details?id=com.icar.ricexpert&hl=en&gl=US)]([https://play.google.com/store/apps/details?id=com.icar.ricexpert&hl=en&gl=US](https://play.google.com/store/apps/details?id=com.icar.ricexpert&hl=en&gl=US)), accessed on 1 November 2021) [23] is a mobile application for disseminating rice plant knowledge, e.g., rice diseases, paddy insects, non-insect pests, weeds, nutrients, as well as trendy news, a fertilizer calculator, and online advisory services from experts via text, images, and voice. Finally, Plantix [24; 25] is a pest and disease diagnosis tool that employs machine learning algorithms to recommend a customized treatment corresponding to an identified disease based on an input image. It can support the knowledge of pests and diseases of more than 46 plants; it also provides a fertilization calculator. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Existing work**} & \multirow{2}{*}{**Domain of Interest**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Farming Knowledge** \\ **System** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Plant Disease** \\ **Diagnosis System** \\ \end{tabular} } & \multirow{2}{*}{**Other Features**} \\ \cline{3-3} \cline{5-6}  & & **Web** & & & & **Web** & **Mobile** \\ \hline \multirow{2}{*}{**PlantVillage**[18] & various crops e.g., & & & & & & \\  & coffee, rice, tea, etc. & & & & & & \\ \hline \multirow{2}{*}{**Plantwise**[19; 20] & various plants & & & & & & \\  & from 34 countries & & & & & & \\ \hline \multirow{2}{*}{**CROPRTECT**[22] & various crops, pests, & & & & & & \\  & weeds, diseases, etc. & & & & & & \\ \hline \multirow{2}{*}{**RiceXpert**[23] & rice & & & & & & news, fertilizer calculator, \\  & rice & & & & & & advisory service, etc. \\ \hline \multirow{2}{*}{**BRRI Rice Knowledge**} & & & & & & \\  & rice & & & & & \\ \hline \multirow{2}{*}{**Rice Doctor**[27; 28] & rice & & & & & & \\ \hline \multirow{2}{*}{**Agrobase**[29] & various crops, pests, weeds, & & & & & \\  & diseases, pesticides, etc. & & & & & \\ \hline \multirow{2}{*}{**Smart Rice Farm v.2**[30] & rice & & & & & \\  & rice & & & & & \\ \hline \multirow{2}{*}{**Plantix**[24; 25] & various plants: apple, & & & & & \\  & cotton, maize, rice, etc. & & & & & \\ \hline \hline \end{tabular} Note that \(\bigstar^{\prime}\) means available, \(\bigstar^{\prime}\) means not available.  
\end{table} 
Table 2: A comparison of online farming knowledge portals and diagnosis systems. Regarding image-based disease detection, ref. [31] reviews various modern feature extraction techniques for plant disease detection. In [32], the authors propose a C-GAN based deep learning approach to detect tomato diseases from leaf images. The residual-based CNN architecture is proposed in [33].