# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 1 Introduction  
Agriculture plays a critical role in the economy of many countries, since it contributes, either in a small or big way, to the production of essential food crops, employment generation as well as national income. The agricultural industry faces challenges such as water shortages, soil fertility, pests and diseases affecting crops, and increasingly rigorous standards on quality and safety food, to mention but a few. Regarding crop pests, these represent one of the major constraints to increase food production [1] since they severely damage crop plants and reduce the quality of food grains and products, which causes considerable economic losses [2]. From this perspective, if invasive pests and pathogens continue spreading, their potential costs to global agriculture could be more than 540 billion dollars per year [3]. An insect pest can be referred to as any insect species whose activities, enhanced by population density, causes economic losses to cultivated crops [4]. Nowadays, there are different methods for insect pests controlling such as good farming practices, breeding and growing resistant varieties, biological control, among others. However, the use of chemical pesticides is the main mode of pest control among farmers [5], which severely increases environmental pollution as well as contributing to the reduction of the population of natural enemies of herbivores [6]. In the agricultural context, there is a great diversity of insects and diseases that affect the crops. Moreover, the amount of data available on data sources such as the Web regarding these topics increase every day. This fact can represent a problem when farmers want to make decisions based on this large and dynamic amount of information. From this perspective, to diagnose the insect pest that is affecting a crop is a serious challenge to farmers since this task demands knowledge and the experience of experts on insect pests. Therefore, there is a need for systems that integrate an expert's knowledge to support farmers to perform diagnosis, control, and management of insect pests aiming to improve the quality of food grain as well as to reduce economic losses. The Semantic Web provides to the Web information with a well-defined meaning which allows humans and computers to understand it. One of the pillars of the Semantic Web are the ontologies, which can be referred as to a formal and explicit specification of a shared conceptualization [7]. Nowadays, decision support systems based on ontologies have been widely accepted as solutions in different domains such as recommender systems [8], software engineering [9], and health [10; 11; 12]. This opens the door for opportunities to apply this technology to the agriculture domain, specifically for crop insect pest's diagnosis and management. This work presents AgriEnt, a knowledge-based Web platform focused on supporting farmers in the decision-making process concerning crop insect pest diagnosis and management. AgriEnt takes advantage of ontologies to formal and explicit describe the agricultural entomology experts' knowledge and to perform insect pest diagnosis through a rule-based inference engine that recommends the appropriate treatments to deal with the diagnosed insect pest, considering the symptoms provided by the user. By using an ontology, it is possible for non-expert people to take advantage of the knowledge of experts on this domain to decide which insect pest is damaging their crops. Furthermore, the Web application provided by this platform makes it possible for farmers to get decision support regardless of where they are since it can be accessed from any device with internet access and a Web browser installed. This paper describes the AgriEnt platform, emphasising the modeling of the ontology for crop insect pest management as well as the implementation of rules based on SWRL (Semantic Web Rule Language) [13] for diagnosing a crop insect pest based on the symptoms provided by the user.