Open AccessAndroid based rice pest detection system using learning vector quantization method
Author(s) -
Arief Budiman,
Pradityo Utomo,
Sri Rahayu
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/293/1/012001
Subject(s) - pest analysis , learning vector quantization , agricultural engineering , paddy field , integrated pest management , livelihood , computer science , agroforestry , business , engineering , agronomy , environmental science , artificial intelligence , agriculture , biology , vector quantization , marketing , ecology
In Indonesia, rice was still an essential commodity. Aside from being a staple food producer of rice, as well as a primary source of livelihood for farmers. The problem often experienced by farmers was the threat of pest attack and cause massive losses; it could even lead to crop failures and harm the farmers. During this time to suppress the attacks of pest, farmers give pesticides which if it handles inappropriate would interfere with health. The best solution was to make detection system of pest attacks so could minimise the pest aggression. The limited knowledge and skills of farmers in the detection of rice crops made farmers prefer to spray pesticides. To facilitate farmers in detecting pest attacks, developed Android-based applications that were simple and easy to use device directly in the field using smartphone devices and Learning Vector Quantization (LVQ) Method. Farmers could use the application by taking pictures of rice plants that allegedly exposed to attacks with smartphones. The app would recommend alternative solutions to pest-infected plants without the use of pesticides.