A review of visible and near-infrared (Vis-NIR) spectroscopy application in plant stress detection

Health monitoring in plants is vital for agricultural sustainability. Currently, the number of techniques able to detect plant stress and disease at an early stage is limited. Prevention of diseases and stress, while the plants are still in an asymptomatic stage could lead to better crop management in agricultural industries. This review focuses on the applications of visible and near-infrared (Vis-NIR) spectroscopy in disease detection and the implications of stress in various species of plants. It is a rapid and non-destructive technique that doesn't require or requires only minimal sample processing before measurements and data analysis. The visible and near-infrared region can detect almost all functional groups and compounds making it a promising tool for data analysis. A brief overview of the methods used and the absorption bands in the Vis-NIR range related to plant disease and stress will be discussed. The comprehensive review of the application of the visible and near-infrared range regions according to different types of disease and stress including the methods used for the data analysis is being addressed

Spectral response to early detection of stressed oil palm seedlings using near-infrared reflectance spectra at region 900-1000 nm

A method was developed based on spectral analysis and classification models for early detection of water stress level in the leaves of oil palm seedlings. The healthy (well-watered: D0) and water-stressed (subjected to water stress for five days: D1-D5) leaves of oil palm seedlings were investigated to identify and classify the stress levels. The stress levels were grouped as light, moderate, and severe. The region 900–1000 nm was selected because it is highly correlated with water content, particularly in terms of first and second derivatives. The measured reflectance spectra at 900–1000 nm were pre-processed using smoothing, standard normal variate (SNV), and first and second Savitzky-Golay (SG) derivatives. Principal component analysis (PCA) was performed on several transformed datasets to reduce the reflectance spectral dimension and derive the principal components (PCs). Support vector machine (SVM) and linear discriminant analysis (LDA) classification models were employed to the scores of PCs to achieve six classification levels of water stress. Classification accuracy was assessed using the overall accuracy and confusion matrix of testing datasets. The SVM and PCA-LDA classification models predicted the water stress levels with high average overall classification accuracy of 92 % and 94 % using the smoothed + SNV + first derivative and smoothed + SNV spectral dataset, respectively. The findings confirmed the potential of 900–1000 nm region to distinguish the different levels of water stress in oil palm seedlings

Review – Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor

Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region

Correlation of Near-Infrared (Nir) Spectroscopy with Water Quality Sensors to Detect Concentration of Saccharomyces Boulardii in Water

Terahertz (THz) sensing of high sensitivity detection has given the possibility of a non-invasive method for measuring and monitoring microorganism from water resources. The purpose of this study is to analyse the effectiveness of Near-infrared (NIR) spectroscopy as a non-destructive and in-situ measurement-based method for detection of Saccharomyces boulardii (S. boulardii) in water. Samplings are prepared in biotechnology lab in Universiti Malaysia Pahang (UMP), a yeast species of S. boulardii is used as a model microorganism. A single colony of yeast was inoculated in liquid broth media and incubated for overnight culture. A standard serial dilution method was applied to prepare five samples at different yeast concentration in corresponding test tubes of 0%, 10%, 20%, 50% and 100%. A hand-held NIR spectroscopy with range from 900nm to 1700nm wavelength is deployed gapless to scan those test tubes through its optical window. Meanwhile another sample with similar concentrations are inoculated into volume 0.0071 m3 of water equipped with water quality sensor system for monitoring and analysis purpose. The findings show inoculation certain concentration of 10%, 20%, 50% and 100% of S. boulardii into the water generated certain level of NIR spectroscopy’s spectral absorbance of 0.723, 0.64, 0.357 and 0.121 correspondingly at 1067 nm wavelength. This proves NIR spectroscopy is a highly-sensitivity THz sensor at 1067 nm wavelength as absorbance is at the minimum level as S. boulardii concentration is at the maximum. This finding is further validated by Dissolved Oxygen (DO) sensor which demonstrates rising maximum of 8 ppm after an hour of S. boulardii’s inoculation compared to 4 ppm in a normal water. However, the DO level back to normal after 5 hours due to the acclimatization process of the yeast and demonstrate capability of DO sensor to detect presence of yeast in water. PCA and PLS analysis based NIR spectroscopy’s spectral absorbance also demonstrates ability to categorise severity of a microbial illness depending on its concentration. The results from this study has suggested that the NIR spectroscopy sensor as an excellent option for microbial sensing in water