PENYEK: Automated brown planthopper detection from imperfect sticky pad images using deep convolutional neural network.
Rice is a staple food in Asia and it contributes significantly to the Gross Domestic Product (GDP) of Malaysia and other developing countries. Brown Planthopper (BPH) causes high levels of economic loss in Malaysia. Identification of BPH presence and monitoring of its abundance has been conducted ma...
Main Authors: | Azree Nazri, Norida Mazlan, Farrah Muharam |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2018-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0208501 |
Similar Items
-
Screening of Brown Planthopper Resistant miRNAs in Rice and Their Roles in Regulation of Brown Planthopper Fecundity
by: Lü Jun, et al.
Published: (2022-11-01) -
Recent Strategies for Detection and Improvement of Brown Planthopper Resistance Genes in Rice: A Review
by: Bello Sani Haliru, et al.
Published: (2020-09-01) -
A rigorous model of the brown planthopper/
by: 307781 Khor, Kok Eam
Published: (1984) -
Transcriptome analysis of the brown planthopper Nilaparvata lugens.
by: Jian Xue, et al.
Published: (2010-01-01) -
Insecticidal effect of aconitine on the rice brown planthoppers.
by: Shuqin Wei, et al.
Published: (2019-01-01)