Data Analysis of Cold Rice Blast Based on Near Infrared Spectroscopy

Tan, Feng; Ma, Xiaodan; Wang, Chun; Shang, Tingyi

doi:10.1007/978-3-642-27278-3_8

Cited by 2 publications

(3 citation statements)

References 3 publications

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“…Some previous work has focused on a combination of pests and diseases, for example in [10] where a multi-spectral imaging methodology for the diagnosis of plant diseases and insect pests was employed. The use of near-infrared spectroscopy to analyze cold rice blast is also discussed in [11]. Similarly, hyper-spectral data were used for the pre-symptomatic detection of infections in sugar beet in [12].…”

Section: Related Workmentioning

confidence: 99%

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

et al. 2021

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We discuss the use of matrix relevance learning, a popular extension to prototype learning algorithms, applied to a three-class classification task of diagnosing cassava diseases from spectral data. Previously this diagnosis has been done using plant image data taken with a smartphone. However for this method disease symptoms need to be visible. Unfortunately for some cassava diseases, once symptoms have manifested on the aerial part of the plant, the root which is the edible part of the plant has been totally destroyed. This research is premised on the hypothesis that diseased crops without visible symptoms can be detected using spectral information, allowing for early interventions. In this paper, we analyze visible and near-infrared spectra captured from leaves infected with two common cassava diseases (cassava brown streak disease and cassava mosaic virus disease) found in Sub-Saharan Africa. We also take spectra from leaves of healthy plants. The spectral data come with thousands of dimensions, therefore different wavelengths are analyzed in order to identify the most relevant spectral bands for diagnosing these disease. To cope with the nominally high number of input dimensions of data, functional decomposition of the spectra is applied. The classification task is addressed using Generalized Matrix Relevance Learning Vector Quantization and compared with the standard classification techniques performed in the space of expansion coefficients.INDEX TERMS Cassava disease diagnosis, feature selection, matrix relevance learning, spectral data.

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Section: Related Workmentioning

confidence: 99%

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

et al. 2021

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“…In addition, the general data systems of spectroscopy consist of a general computer connected to a printer that can produce high-quality spectra, as well as a software programme that includes spectrum acquisition offer, spectral transform, spectral processing, spectral analysis, and all other functions. Besides, users can also create their own operational procedures of spectral data for their needs (Tan et al, 2012).…”

Section: Spectroscopy-based Techniquesmentioning

confidence: 99%

“…The strength of FTIR is its speed advantage which makes it possible to obtain spectra on a millisecond timescale. Tan et al (2012) employed the NIR spectroscopy for detection of cold rice blast in cold land, which consisted of the healthy rice plant, three diseased levels of leaves blast, five diseased levels of grain blast, and four diseased levels of the infected stem. The study adopted the multi-spectral vision technology to investigate the identified classification test on the rice varieties, which had been resistance to disease.…”

Section: Spectroscopy-based Techniquesmentioning

confidence: 99%

Optical imaging techniques for rice diseases detection: A review

2020

JAFE

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Rice diseases have caused great economic losses to farmers in rice cultivation. The current assessment of rice disease evaluation still relies on manual, subjective, and laborious techniques. The manual and subjective evaluations lead to uncertainties since some diseases have almost similar characterisation. The applications of immunological, molecular, and microscope techniques are time-consuming, costly, and skills dependent. Thus, optical techniques are recommended to facilitate the control of diseases through their feasibility, rapidity, and accuracy, which can lead to better management strategies, besides improving production activity. These techniques for detecting and monitoring the diseases are important for precaution and prevention action. The present review discusses the existing and potential optical techniques for the detection of rice diseases. The techniques include optical imaging that consists of computer vision, spectroscopy, multispectral imaging, hyperspectral imaging (HSI), and remote sensing. Thus, this work presents in-depth information related to the nondestructive and potential applications of optical imaging techniques for rice disease detection.

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Data Analysis of Cold Rice Blast Based on Near Infrared Spectroscopy

Cited by 2 publications

References 3 publications

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

Matrix Relevance Learning From Spectral Data for Diagnosing Cassava Diseases

Optical imaging techniques for rice diseases detection: A review

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