Early pest detection in crops is a crucial aspect of agriculture that can significantly impact crop yields. This project aims to develop an effective pest detection system by utilizing a symptom-based weighting method combined with the Breadth-First Search (BFS) algorithm. The system integrates three approaches to calculate symptom weights: expert judgment, symptom ranking, and pairwise comparison, resulting in a final weight for each symptom. Additionally, the system applies the Certainty Factor (CF) method to calculate the level of confidence in pest detection based on observed symptoms. The implementation of the BFS algorithm enables efficient pest identification with a minimum symptom match threshold of 70%. The results show that symptom weights and certainty levels can provide accurate information about the pests affecting crops. Therefore, the system not only assists farmers in identifying pests but also offers appropriate treatment recommendations. This project is expected to serve as a valuable tool in pest management, enhance agricultural productivity, and reduce losses caused by pest infestations.
Keywords
BFS Algorithm, Symptom Weighting, Certainty Factor, Pest Detection, Pest Management, Agriculture, Agricultural Information System.
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Implementation of Breadth-First Search and Certainty Factor in an Expert System for Coffee Plant Pest Detection
Rahma Yunita, I Nurul Kholidiah, Dwi Utami
= https://doi.org/10.26753/dns.v1i2.1558
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