@inproceedings{50971859b9ed4b0c80d714051455e818,
title = "Detection of Fusarium wilt on Phalaenopsis stem base region using band selection techniques",
abstract = "Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt.",
keywords = "Band selection, CEM, Fusarium wilt, Hyperspectral image, OSP, Phalaenopsis, SVM",
author = "Lee, {Meng Chueh} and Ma, {Kenneth Yeonkong} and Ouyang, {Yen Chieh} and Mang Ou-Yang and Guo, {Horng Yuh} and Liu, {Tsang Sen} and Chen, {Hsian Min} and Wu, {Chao Cheng} and Chang, {Chgein I.}",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/IGARSS.2018.8517781",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2777--2780",
booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings",
address = "United States",
note = "null ; Conference date: 22-07-2018 Through 27-07-2018",
}