Semi-supervised learning of hyperspectral image segmentation applied to vine tomatoes and table grapes

Roy, Jeroen van; Wouters, Niels; Ketelaere, Bart De; Saeys, Wouter

doi:10.1255/jsi.2018.a7

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…Previous studies have utilized hyperspectral imaging for detecting diseases in other crops, such as tea plants, tomatoes [9], and more [10,11]. For example, Lin et al [12] used hyperspectral imaging to detect anthracnose in tea plants and achieved an overall accuracy of 98% for identifying the disease at the leaf level.…”

Section: Introductionmentioning

confidence: 99%

The Automated Detection of Fusarium Wilt on Phalaenopsis Using VIS-NIR and SWIR Hyperspectral Imaging

Shih,

Chang,

Chou

et al. 2023

Remote Sensing

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Phalaenopsis, an essential flower for export, is significantly affected by fusarium wilt, which impacts its export quality. Hyperspectral imaging technology offers the potential to detect fusarium wilt on Phalaenopsis. The goal of this study was to establish an automated platform for the rapid detection of fusarium wilt on Phalaenopsis. In this research, the automatic target generation process (ATGP) method was employed to identify outliers in the hyperspectral spectrum. Subsequently, the Spectral Angle Mapper (SAM) method was utilized to detect signals similar to the outliers. To suppress background noise and extract the region of interest (ROI), the Constrained Energy Minimization (CEM) method was implemented. For ROI classification and detection, a deep neural network (DNN), a support vector machine (SVM), and a Random Forest Classifier (RFC) were employed. Model performance was evaluated using three-dimensional receiver operating characteristics (3D ROC), and the automated identification system was integrated into hyperspectrometers. The proposed system achieved an accuracy of 95.77% with a total detection time of 3380 ms ± 86.36 ms, proving to be a practical and effective tool for detecting fusarium wilt on Phalaenopsis in the industry.

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Section: Introductionmentioning

confidence: 99%