Detection and analysis of phalaenopsis fusarium wilt using machine learning

In this paper, we build a platform that can automatically and rapidly detect Fusarium wilt on Phalaenopsis. We have also developed a portable handheld multispectral imaging device (PHMID) that contains six LEDs representing six spectral bands, making it easier to use in the field. The Automatic Target Generation Process (ATGP) and the Spectral Angle Mapper (SAM) are used to obtain the desired signal on a high-spectral image. The Harsany-Farrand-Chang (HFC) method is used for band selection to estimate the number of different spectral bands. We use deep neural networks (DNNs), support vector machines (SVMs), and random forest classifiers (RFCs) for classification. The best detection accuracy of VNIR, SWIR and PHMID was 95.77%, 91.72% and 90.84%, respectively.