Plant cells, tissues and organs are composed of various biomolecules arranged as structurally diverse units, which represent heterogeneity at microscopic levels. Molecular knowledge about those constituents with their localization in such complexity is very crucial for both basic and applied plant sciences. In this context, infrared imaging techniques have advantages over conventional methods to investigate heterogeneous plant structures in providing quantitative and qualitative analyses with spatial distribution of the components. Thus, particularly, with the use of proper analytical approaches and sampling methods, these technologies offer significant information for the studies on plant classification, physiology, ecology, genetics, pathology and other related disciplines. This review aims to present a general perspective about near-infrared and mid-infrared imaging/microspectroscopy in plant research. It is addressed to compare potentialities of these methodologies with their advantages and limitations. With regard to the organization of the document, the first section will introduce the respective underlying principles followed by instrumentation, sampling techniques, sample preparations, measurement, and an overview of spectral pre-processing and multivariate analysis. The last section will review selected applications in the literature.

A Review of Mid-Infrared and Near-Infrared Imaging: Principles, Concepts and Applications in Plant Tissue Analysis

Background
Combined assessment of leaf reflectance and transmittance is currently limited to spot (point) measurements. This study introduces a tailor-made hyperspectral absorption-reflectance-transmittance imaging (HyperART) system, yielding a non-invasive determination of both reflectance and transmittance of the whole leaf. We addressed its applicability for analysing plant traits, i.e. assessing Cercospora beticola disease severity or leaf chlorophyll content. To test the accuracy of the obtained data, these were compared with reflectance and transmittance measurements of selected leaves acquired by the point spectroradiometer ASD FieldSpec, equipped with the FluoWat device.

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HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging.

A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy

Detection of nutrition deficiencies in plants using proximal images and machine learning: A review

Visible/near-infrared (Vis/NIR) hyperspectral imaging was employed to determine the spatial distribution of total nitrogen in pepper plant. Hyperspectral images of samples (leaves, stems, and roots of pepper plants) were acquired and their total nitrogen contents (TNCs) were measured using Dumas combustion method. Mean spectra of all samples were extracted from regions of interest (ROIs) in hyperspectral images. Random frog (RF) algorithm was implemented to select important wavelengths which carried effective information for predicting the TNCs in leaf, stem, root, and whole-plant (leaf-stem-root), respectively. Based on full spectra and the selected important wavelengths, the quantitative relationships between spectral data and the corresponding TNCs in organs (leaf, stem, and root) and whole-plant (leaf-stem-root) were separately developed using partial least-squares regression (PLSR). As a result, the PLSR model built by the important wavelengths for predicting TNCs in whole-plant (leaf-stem-root) offered a promising result of correlation coefficient (R) for prediction (RP = 0.876) and root mean square error (RMSE) for prediction (RMSEP = 0.426%). Finally, the TNC of each pixel within ROI of the sample was estimated to generate the spatial distribution map of TNC in pepper plant. The achievements of the research indicated that hyperspectral imaging is promising and presents a powerful potential to determine nitrogen contents spatial distribution in pepper plant.

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Hyperspectral imaging for mapping of total nitrogen spatial distribution in pepper plant.

Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging

In protected agriculture, deficiency of an essential element may drastically affect plant growth, appearance and most importantly yield. Information about nutrient deficiencies in crops grown in controlled environment is essential to optimize food productivity. In this study, near infrared reflectance spectroscopy (NIRS) analysis was used to identify nitrogen (N) deficiency coupled with pattern recognition methods in mini-cucumber plants grown under non-soil conditions. Leaves at the first three nodes of nitrogen deficient plants and control plant were used for NIRS data acquisition. K-nearest neighbors (KNN) and artificial neural network (ANN) were applied to build diagnostics models, respectively. Some parameters of the model were optimized by cross-validation. The performance of the KNN model and the ANN model based on NIRS data was compared. Experiment results showed that the ANN model was better than the KNN model. The optimal ANN model was achieved when principle component factors were equal to 5 and identification rate of the ANN model were 100% in both the training set and the prediction set. This study demonstrated that the NIRS coupled with ANN pattern recognition method can be successfully applied to the diagnostics of nitrogen deficiency in minicucumber plant grown under non-soil conditions. Key words : Deficiency, nitrogen, near infrared reflectance spectroscopy (NIRS), artificial neural network.

/pdf/diagnostics-of-nitrogen-deficiency-in-mini-cucumber-plant-by-1m29q9066w.pdf

Diagnostics of nitrogen deficiency in mini-cucumber plant by near infrared reflectance spectroscopy

Leaf chlorophyll content provides valuable information about physiological status of plants. NIR spectroscopy measurement makes it possible to quickly and non-destructively assess the chlorophyll content in leaves. Our objective was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation in cucumber leaves. Simulated annealing algorithm (SAA) was used to select most efficient wavelength for cucumber leaf chlorophyll content. 24 wave numbers of cucumber leaves spectral were selected by simulated annealing algorithm. With this combination, partial least square (PLS) model was obtained with the correlation coefficient (Rc) of 0.9542, root mean square error of calibration (RMSEC) of 0.165 and the prediction coefficient (Rp) of 0.9397, root mean square error of prediction (RMSEP) of 0.173. The results of the study shows that NIR spectroscopy could used to measure chlorophyll content of cucumber leaves and SAA could improve the simplify and accuracy of chlorophyll models.

Chen Zheng-wei

Papers

Nondestructive diagnostics of nitrogen deficiency by cucumber leaf chlorophyll distribution map based on near infrared hyperspectral imaging

Diagnostics of nitrogen deficiency in mini-cucumber plant by near infrared reflectance spectroscopy

Non-destructive measurement of cucumber leaf chlorophyll content by NIR spectroscopy based on simulated annealing algorithm