# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 5 Conclusions  
Damage by insect pests is a serious challenge to agricultural producers, governments, and researchers. From this perspective, the integration of new technologies in insect pest control must be explored aiming to develop and implement safely, effectively and sustainably solutions. Based on the literature review presented in Section 2, we can conclude that there are relatively few research efforts for developing ontology-based decision support systems for crop insect pests' control and management. To address this issue, we presented AgriEnt, a Web platform that uses ontologies for decision support purposes regarding crop insect pest management. Thanks to ontologies, the expert knowledge on insect pests that affect Ecuadorian crops is available for the non-expert users to diagnoses, control, and manage insect pests. The AgriEnt platform, through the use of the ontology, has proven to produce similar answers as the professional advice given by the entomology experts involved in the evaluation process.  
Although the current version of AgriEnt obtained encouraging results, it has several issues that must be addressed to provide better support to farmers. For instance, despite the AgriEnt platform is centered in a specific group of insect pests, it can be easily extended in order to deal with other insect pests from other regions. However, as more crops, diseases, and insect pests are included in the ontology, the number of rules must increase. In this sense, machine-learning methods should be investigated to discover automatically patterns in the data collected by farmers and experts and then make predictions for answering questions from the crop insect pest-management domain. On the other hand, as the current platform infers the insect pest based on the symptoms perceived by the user, this information could be inaccurate since it depends on the farmer's expertise detecting symptoms i.e., they are not able to identify specific symptoms that may affect the diagnosis accuracy. To address this issue, we plan to integrate computer vision technologies to make the symptom collections independent of the farmer's expertise i.e. using photographs of the crops to obtain more precise information about the symptoms could help to achieve a better diagnosis.  
Author Contributions: Conceptualization, K.L.-O. and R.V.-G.; methodology, M.A.P.-V. and K.L.-O.; software, K.L.-O. and J.A.G.-D.; validation, K.L.-O., J.A.G.-D., and R.V.-G.; formal analysis, M.P.S.-Z.; investigation, M.A.P.-[PERSON]; resources, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]-V., [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
Figure 4: Technology acceptance model (TAM) results (*** \(p<0.01\), *** \(p<0.001\)).  
**Funding:** This work has been partially supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through project KBS4 FIA (TIN2016-76323-R), and Seneca Foundation-the Regional Agency for Science and Technology of Murcia (Spain)- through project 20963/PI/18.  
**Acknowledgments:** This work was supported by the National Technological Institute of Mexico (TecNM) and sponsored by Mexico's National Council of Science and Technology (CONACYT).  
**Conflicts of Interest:** The authors declare no conflict of interest.