Abstract
In the agricultural context, there is a great diversity of insects and diseases that affect 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. 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 relies on a layered functional architecture comprising four layers: the data layer, the semantic layer, the web services layer, and the presentation layer. This platform takes advantage of ontologies to formally and explicitly describe agricultural entomology experts’ knowledge and to perform insect pest diagnosis. Finally, to validate the AgriEnt platform, we describe a case study on diagnosing the insect pest affecting a crop. The results show that AgriEnt, 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. Therefore, this platform can guide farmers to make better decisions concerning crop insect pest diagnosis and management.
Highlights
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 rule-based inference engine allows AgriEnt to analyze crop symptoms collected by farmers aiming to contribute to the insect pest diagnosis and their management
AgriEnt obtained a 0.8221 score of accuracy in the reasoning of crop insect pest diagnosis, producing correct diagnosis in 123 test cases for all crops considered in this version
Summary
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]. 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, for crop insect pest’s diagnosis and management.
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