Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/538
Title: ESforRPD2 : Expert System for Rice Plant Disease Diagnosis
Authors: Agus, Fahrul
Ihsan, Muh
Khairina, Dyna Marisa
Candra, Krishna Purnawan
Issue Date: 2019
Publisher: F1000Research
Abstract: One of the factors causing rice production disturbance in Indonesia is that farmers lack knowledge of early symptoms of rice plant diseases. These diseases are increasingly rampant because of the lack of experts. This study aimed to overcome this problem by providing an Expert System that helps farmers to make an early diagnosis of rice plant diseases. Data of rice plant pests and diseases in 2016 were taken from Samarinda, East Kalimantan, Indonesia using an in-depth survey, and rice experts from the Department of Food Crops and Horticulture of East Kalimantan Province were recruited for the project. The Expert System for Rice Plant Disease Diagnosis, ESforRPD2, was developed based on the pest and disease experiences of the rice experts and uses a Waterfall Paradigm and Unified Modeling Language. This Expert System can detect 48 symptoms and 8 types of diseases of rice plants from 16 data tests with a sensitivity of 87.5%. ESforRPD2 is available in Indonesian at http://esforrpd2.blog.unmul.ac.id
URI: http://repository-ds.unmul.ac.id:8080/handle/123456789/538
ISSN: 2046-1402
Appears in Collections:J - Computer Sciences and Information Technology

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