Diagnosis of Tobacco Plant Pests and Diseases Using the Forward Chaining Method

Tobacco is a plantation commodity that is susceptible to pests and diseases, such as Phytophthora nicotianae (lanas disease), Myzus persicae (aphids), or Cercospora nicotianae (leaf spots). Lack of farmer knowledge in identifying early symptoms often leads to inappropriate handling and economic losses. This study aims to develop an expert system based on the Forward Chaining method to diagnose tobacco plant pests and diseases quickly and accurately. Symptom data are collected through field observations and literature and then represented as rule-based knowledge (for example, "IF leaves with yellow spots AND brown spots in the middle THEN Cercospora nicotianae"). The Forward Chaining method makes inferences by matching user input facts (symptoms) to existing rules to reach conclusions. The system was tested using 50 field cases with an accuracy of 85% compared to manual diagnosis by experts.

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Publication Details

Journal
Journal of Advances in Information and Industrial Technology
Published
2025-05-23
DOI
https://doi.org/10.52435/jaiit.v7i1.629
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Diagnosis of Tobacco Plant Pests and Diseases Using the Forward Chaining Method

Kemal Farouq Mauladi, Suziyanti Marjudi, Arief Budi Laksono
Journal of Advances in Information and Industrial Technology
Smart Agriculture and AI
article

Diagnosis of Tobacco Plant Pests and Diseases Using the Forward Chaining Method

Kemal Farouq Mauladi, Suziyanti Marjudi, Arief Budi Laksono
article en

Abstract

Tobacco is a plantation commodity that is susceptible to pests and diseases, such as Phytophthora nicotianae (lanas disease), Myzus persicae (aphids), or Cercospora nicotianae (leaf spots). Lack of farmer knowledge in identifying early symptoms often leads to inappropriate handling and economic losses. This study aims to develop an expert system based on the Forward Chaining method to diagnose tobacco plant pests and diseases quickly and accurately. Symptom data are collected through field observations and literature and then represented as rule-based knowledge (for example, "IF leaves with yellow spots AND brown spots in the middle THEN Cercospora nicotianae"). The Forward Chaining method makes inferences by matching user input facts (symptoms) to existing rules to reach conclusions. The system was tested using 50 field cases with an accuracy of 85% compared to manual diagnosis by experts.

Journal of Advances in Information and Industrial TechnologyVol. 7(1)
Universitas Islam Lamongan (ID), Tun Hussein Onn University of Malaysia (MY)
Openalex Percentile: Top 90%
Smart Agriculture and AI
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