TIIS (한국인터넷정보학회)
Current Result Document : 80 / 80
| 한글제목(Korean Title) |
Pest Prediction in Rice using IoT and Feed Forward Neural Network |
| 영문제목(English Title) |
Pest Prediction in Rice using IoT and Feed Forward Neural Network |
| 저자(Author) |
Muhammad Salman Latif
Rafaqat Kazmi
Nadia Khan
Rizwan Majeed
Sunnia Ikram
Malik Muhammad Ali-Shahid
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| 원문수록처(Citation) |
VOL 15 NO. 12 PP. 0133 ~ 0152 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2nd largest crop being produced and 3rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer’s palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate. |
| 키워드(Keyword) |
Internet of things (IoT)
Stem Borer Pest Prediction
Artificial Neural Network
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| 파일첨부 |
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