Expert System for Diagnosing Plant-disturbing Organisms on Rice Plants Using the Euclidean Probability Method and Bayes Theorem with Forward Chaining Inference Technique

Abstract

Rice is a basic human need that needs to be fulfilled continuously, especially in Indonesia. However, rice production decreased by 2.05% in 2023; the decline was influenced by the lack of rice fields and crop failure due to attacks by plant-disturbing organisms such as Blast, Brown Spot, and even Ricefield Rats. Therefore, expert system technology is useful to help create opportunities for progress in the agricultural sector in overcoming the decline in production. This research utilizes the best method between Euclidean Probability, Bayes` Theorem, and a combination of both in diagnosing plant-disturbing organisms in rice plants. The expert system works by analyzing the symptoms and characteristics of the plants using weight values obtained from the Analytical Hierarchy Process, comparing them with a database of known plant-disturbing organisms, and providing accurate diagnoses and management recommendations. The objectives are to determine which method provides the most accurate diagnosis and to explore how these methods can support sustainable agriculture. The combination of Bayes' theorem with Euclidean methods and Bayes' theorem alone achieved an agreement of 8 out of 10 cases with expert diagnoses. In comparison, the Euclidean method alone achieved an agreement of 9 out of 10 cases. The results demonstrate that the Euclidean Probability method offers a more accurate diagnosis, aligning with expert diagnoses in 9 of the 10 case studies, thus supporting its application in sustainable agricultural practices.