Browsing by Author "Liu, Tsang-Sen"

Now showing 1 - 2 of 2

DETECTION OF FUSARIUM WILT ON PHALAENOPSIS STEM BASE REGION USING BAND SELECTION TECHNIQUES
(2018-01-01)
Lee, Meng-Chueh
;
Ma, Kenneth-Yeonkong
;
Ouyang, Yen-Chieh
;
Mang Ou-Yang
;
Guo, Horng-Yuh
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.
QUALITY INSPECTION OF PHALAENOPSIS HYBRIDS USING HYPERSPECTRAL BAND SELECTION TECHNIQUES
(2019-01-01)
Ouyang, Yen-Chieh
;
Chen, Bo-Han
;
Lee, Meng-Chueh
;
Liu, Tsang-Sen
;
Ou-Yang, Mang
Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.