Hyperspectral Reflectance Imaging Research Articles

Classification of tree species based on hyperspectral reflectance images of stem bark

ABSTRACT Automatization of tree species identification in the field is crucial in improving forest-based bioeconomy, supporting forest management, and facilitating in situ data collection for remote sensing applications. However, tree species recognition has never been addressed with hyperspectral reflectance images of stem bark before. We investigated how stem bark texture differs between tree species using a hyperspectral camera set-up and gray level co-occurrence matrices and assessed the potential of using reflectance spectra and texture features of stem bark to identify tree species. The analyses were based on 200 hyperspectral reflectance data cubes (415–925 nm) representing ten tree species. There were subtle interspecific differences in bark texture. Using average spectral features in linear discriminant analysis classifier resulted in classification accuracy of 92–96.5%. Using spectral and texture features together resulted in accuracy of 93–97.5%. With a convolutional neural network, we obtained an accuracy of 94%. Our study showed that the spectral features of stem bark were robust for classifying tree species, but importantly, bark texture is beneficial when combined with spectral data. Our results suggest that in situ imaging spectroscopy is a promising sensor technology for developing accurate tree species identification applications to support remote sensing.

Identification of Chilling Injury in Kiwifruit Using Hyperspectral Structured-Illumination Reflectance Imaging System (SIRI) with Support Vector Machine (SVM) Modelling

Accurate detection of chilling injury in kiwifruit is challenging because the symptoms are mainly manifested in the interior. This work reports a method for detecting the chilling injury of ‘Hongyang’ kiwifruit to provide nondestructive discrimination. Kiwifruit samples with varying levels of chilling injury were analyzed by a hyperspectral structured-illumination reflectance imaging (SIRI) system. After demodulation, direct current (DC) and alternating current (AC) images with spatial frequencies of 30, 60, and 120 m−1 were obtained and labeled as F30, F60, and F120. Predictive models were developed to optimize the preprocessing and modeling methods. Prediction models established the results of DC and AC with different spatial frequencies and were compared. The autoscale-support vector machine (SVM) models were optimal for AC at different spatial frequencies, and the multiplicative scatter correction (MSC)-SVM model was optimal for DC. The combined features of F30, F60, and F120, as well as the spectral features of DC, had better accuracy for classifying chilling injury. The optimal model of hyperspectral SIRI system for detecting chilling injury was the F30 based on combined features, with calibration accuracy of 98.1% and prediction accuracy of 94.2%. This study has shown that structured illumination had higher accuracy than uniform illumination in predicting chilling injury. Further, this approach allows the identification of kiwifruit with chilling injury using a hyperspectral structured-illumination reflectance imaging system.

Using Colour as a Marker for Coral ‘Health’: A Study on Hyperspectral Reflectance and Fluorescence Imaging of Thermally Induced Coral Bleaching

Rising oceanic temperatures create more frequent coral bleaching events worldwide and as such there exists a need for rapid, non-destructive survey techniques to gather greater and higher definition information than that offered by traditional spectral based monitoring systems. Here, we examine thermally induced laboratory bleaching of Montipora capricornis and Montipora confusa samples, utilising hyperspectral data to gain an understanding of coral bleaching from a spectral standpoint. The data revealed several characteristic spectral peaks that can be used to make health determinations. The fluorescence peaks are attributed to fluorescent proteins (FPs) and Chlorophyll-a fluorescence. The reflectance peaks can be attributed to Chlorophyll absorption and accessory pigments such as Peridinin and Diadinoxanthin. Each characteristic spectral peak or ‘marker’ allows for observation of each aspect of coral health and hence, simultaneous monitoring of these markers using hyperspectral imaging techniques provides an opportunity to better understand the processes occurring during bleaching and the rates at which they occur relative to one another.

Validation of a Hyperspectral Imaging System for Color Measurement of In-Vivo Dental Structures.

A full comprehension of colorimetric relationships within and between teeth is key for aesthetic success of a dental restoration. In this sense, hyperspectral imaging can provide point-wise reliable measurements of the tooth surface, which can serve for this purpose. The aim of this study was to use a hyperspectral imaging system for the colorimetric characterization of 4 in-vivo maxillary anterior teeth and to cross-check the results with similar studies carried out with other measuring systems in order to validate the proposed capturing protocol. Hyperspectral reflectance images (Specim IQ), of the upper central (UCI) and lateral incisors (ULI), were captured on 30 participants. CIE-L*a*b* values were calculated for the incisal (I), middle (M) and cervical (C) third of each target tooth. ΔEab* and ΔE00 total color differences were computed between different tooth areas and adjacent teeth, and evaluated according to the perceptibility (PT) and acceptability (AT) thresholds for dentistry. Non-perceptible color differences were found between UCIs and ULIs. Mean color differences between UCI and ULI exceeded AT (ΔEab* = 7.39-7.42; ΔE00 = 5.71-5.74) in all cases. Large chromatic variations between I, M and C areas of the same tooth were registered (ΔEab* = 5.01-6.07 and ΔE00 = 4.07-5.03; ΔEab* = 5.80-8.16 and ΔE00 = 4.37-5.15; and ΔEab* = 5.42-5.92 and ΔE00 = 3.87-4.16 between C and M, C and I and M and I, respectively). The use of a hyperspectral camera has proven to be a reliable and effective method for color evaluation of in-vivo natural teeth.

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.

.SignificanceHyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.AimTo compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.ApproachWe compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min–max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope—while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.ConclusionsPreprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min–max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

Application of near-infrared hyperspectral imaging coupled with chemometrics for rapid and non-destructive prediction of protein content in single chickpea seed

Evaluating the protein content of a single chickpea seed in a rapid, non-destructive, and precise manner is crucial for facilitating the breeding of high-protein chickpeas. This study explored the potential of near-infrared (NIR) hyperspectral imaging (HSI) to predict the protein content in a single chickpea seed. Eight varieties of chickpeas with different protein contents were subjected to NIR reflectance hyperspectral imaging in the spectral range of 900–2500 nm at two different positions of chickpea seed (micropyle down and micropyle up). The spectral data was correlated with the measured reference protein content of chickpea seed for building the partial least square regression (PLSR) and support vector machine regression (SVMR) models based on different spectral preprocessing techniques, with full spectrum and effective wavelengths selected using competitive adaptive reweighted sampling (CARS) and iteratively retaining informative variables (IRIV) algorithms. When using the full spectrum, the optimal protein prediction model was obtained using PLSR, which yielded correlation coefficient of prediction (R2p) and root mean square error of prediction (RMSEP) values of 0.935 and 0.987, respectively, with external parameter orthogonalization (EPO)+standard normal variate (SNV) preprocessing for micropyle down position of chickpea seed. The IRIV selected wavelength with PLSR yielded the best model with R2p and RMSEP of 0.947 and 0.861, respectively, at the micropyle down position of chickpea seed. Hence, the optimal prediction models were obtained using PLSR with EPO+SNV at the micropyle down position of chickpea seed.

Comparison of Hyperspectral Imagery and Physiological Characteristics of Bentazone-Tolerant and -Susceptible Soybean Cultivars

Bentazone is a broadleaf post-emergence herbicide widely used for crop production that inhibits photosynthetic activity, resulting in phytotoxicity and injury in plants. Evaluating and identifying herbicide-tolerant genotypes is a critical step in plant breeding programs. In this study, we determined the reaction of 138 Korean soybean cultivars to bentazone using visual evaluation, and selected cultivars were further evaluated to determine the effects of bentazone on physiological parameters. For physiological parameters, we measured the normalized difference vegetation index (NDVI) from hyperspectral reflectance images. From 2 to 4 DAT, the NDVI for two sensitive cultivars was between 0.60 and 0.69, while the NDVI for tolerant cultivars was between 0.70 and 0.86. Photosynthesis rate (A), transpiration (E), stomatal conductance (gsw), and total conductance of CO2 (gtc) were measured using chlorophyll fluorescence. Visual score evaluation showed that moderate bentazone-tolerant cultivars were predominant among the Korean cultivars. For physiological measurements, differences in NDVI were detected between bentazone-tolerant and -sensitive cultivars 2 days after treatment (DAT). However, the A, E, gsw, and gtc levels dramatically decreased 1 DAT in the sensitive cultivars. This study provides insights into the tolerance and sensitivity of soybeans to bentazone.

Determination of spectral resolutions for multispectral detection of apple bruises using visible/near-infrared hyperspectral reflectance imaging

This study demonstrates a method to select wavelength-specific spectral resolutions to optimize a line-scan hyperspectral imaging method for its intended use, which in this case was visible/near-infrared imaging-based multiple-waveband detection of apple bruises. Many earlier studies have explored important aspects of developing apple bruise detection systems, such as key wavelengths and image processing algorithms. Despite the endeavors of many, development of a real-time bruise detection system is not yet a simple task. To overcome these problems, this study investigated selection of optimal wavelength-specific spectral resolutions for detecting bruises on apples by using hyperspectral line-scan imaging with the Random Track function for non-contiguous partial readout, with two experimental parts. The first part identified key-wavelengths and the optimal number of key-wavelengths to use for detecting low-, medium-, and high-impact bruises on apples. These parameters were determined by principal component analysis (PCA) and sequential forward selection (SFS) with four classification methods. The second part determined the optimal spectral resolution for each of the key-wavelengths by selecting and evaluating 21 combinations of exposure time and key-wavelength bandwidths, and then selecting the best combination based on the bruise detection accuracies achieved by each classification method. Each of the four classification methods was found to have a different optimized resolution for high accuracy bruise detection, and the optimized resolutions also allowed for use of shorter exposure times. The results of this work can be used to help develop multispectral imaging systems that provide rapid, cost-effective post-harvest processing to identify bruised apples on commercial processing lines.

Health Status of Oilseed Rape Plants Grown under Potential Future Climatic Conditions Assessed by Invasive and Non-Invasive Techniques

Environmental conditions affect many plant traits such as biochemistry, physiology, morphology, and even their distribution around the world. Human activities have increased greenhouse gas emissions, which will promote a global rise in temperatures. The impact of climate change on natural vegetation and crops is difficult to predict, making it necessary to conduct experiments that mimic potential future climate conditions. Here, oilseed rape has been grown under environmental conditions that reproduce severe and intermediate climate change, setting the current climatic conditions as a control, with the main objective of evaluating the impact of climate change on the health status of this plant of agronomic interest. For such a purpose, two approaches (invasive and non-invasive) have been applied. Invasive quantitative measurements are based on the absorbance of biochemical compounds. Non-invasive methods such as thermal, multicolor fluorescence, and hyperspectral reflectance imaging sensors rely on the spectral properties of the plants. The results revealed that climate change induced lipid peroxidation, as well as alterations in pigment composition, transpiration, photosynthesis, and secondary plant metabolism. Those changes were more drastic the more severe the climatic condition imposed. Novel vegetation indices obtained from hyperspectral reflectance and specifically tailored to detect stress in brassicas correlated with physiological traits such as lipid peroxidation and secondary plant metabolism.

Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading.

Bruising of the subcutaneous tissues of blueberries is an important form of mechanical damage. Different levels of bruising have a significant effect on the post-harvest marketing of blueberries. To distinguish different grades of blueberry bruises and explore the effects of different factors, explicit dynamic simulation and near-infrared hyperspectral reflectance imaging were employed without harming the blueberries in this study. Based on the results of the compression experiment, an explicit dynamic simulation of blueberries was performed to measure the potential locations of bruises and preliminarily divide the bruise stages. A near-infrared hyperspectral reflectance imaging system was used to detect the actual blueberry bruises. According to the blueberry photos taken by the near-infrared hyperspectral reflectance imaging system, the actual bruise rates of blueberries were obtained by using the Environment for Visualizing Images software for training and classification. Bruise grades of blueberries were divided accordingly. Response surface methodology was used to determine the effects of ripeness, loading speed and loading location on the blueberry bruising rate. Under the optimized parameters, the actual damage rate of blueberries was 1.1%. The results provide an important theoretical basis for the accurate and rapid identification and classification of blueberry bruise damage.

Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods

The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

Oxygen saturation measurements using novel diffused reflectance with hyperspectral imaging: Towards facile COVID-19 diagnosis

Oxygen saturation level plays a vital role in screening, diagnosis, and therapeutic assessment of disease’s assortment. There is an urgent need to design and implement early detection devices and applications for the COVID-19 pandemic; this study reports on the development of customized, highly sensitive, non-invasive, non-contact diffused reflectance system coupled with hyperspectral imaging for mapping subcutaneous blood circulation depending on its oxygen saturation level. The forearm of 15 healthy adult male volunteers with age range of (20–38 years) were illuminated via a polychromatic light source of a spectrum range 400–980 nm. Each patient had been scanned five times to calculate the mean spectroscopic reflectance images using hyperspectral camera. The customized signal processing algorithm includes normalization and moving average filter for noise removal. Afterward, employing K-means clustering for image segmentation to assess the accuracy of blood oxygen saturation (SpO2) levels. The reliability of the developed diffused reflectance system was verified with the ground truth technique, a standard pulse oximeter. Non-invasive, non-contact diffused reflectance spectrum demonstrated maximum signal variation at 610 nm according to SpO2 level. Statistical analysis (mean, standard deviation) of diffused reflectance hyperspectral images at 610 nm offered precise calibrated measurements to the standard pulse oximeter. Diffused reflectance associated with hyperspectral imaging is a prospective technique to assist with phlebotomy and vascular approach. Additionally, it could permit future surgical or pharmacological intercessions that titrate or limit ischemic injury continuously. Furthermore, this technique could offer a fast reliable indication of SpO2 levels for COVID-19 diagnosis.

Mapping crop residue cover using Adjust Normalized Difference Residue Index based on Sentinel-2 MSI data

Crop residues are effective for the prevention of soil erosion. The crop residue cover (CRC) can be mapped by remote sensing. Different morphologies of crop residue will affect the spectral reflectance, reducing the accuracy of CRC estimation by multispectral data. However, the influence of residue morphology is not fully considered on the accuracy of CRC mapping using satellite images. In addition, the spectral indices are easily saturated and less sensitive to high-density areas of crop residue. This study selected four maize planting sites to obtain hyperspectral reflectance and unmanned aerial vehicle (UAV) images. The effects of CRC and residue morphology on spectral reflectance were analyzed, and a new Residue Adjust Normalized Difference Residue Index (RANDRI) was proposed. UAV images were used to extract ground CRC data for training a CRC prediction model based on Sentinel-2 MSI data. Finally, the piecewise prediction model based on different residue indices was used to map CRC. The study results highlighted a linear relationship between the reflectance intersection of shortwave infrared 2 reflectance and red edge 3 of Sentinel-2 MSI with different residual morphologies, called the residue line. The model accuracy of RANDRI optimized by residual line parameters was better than that of the Normalized Difference Residue Index and Soil Adjust Normalized Difference Residue Index (SANDRI) in the high-density area of crop residue. RANDRI can weaken the influence of residue morphologies on modeling accuracy. The CRC spatial distribution by the piecewise SANDRI+RANDRI model was more consistent with CRC measured than that of the RANDRI models individually. The determination coefficient of the piecewise model was 0.82, and the relative error was 10.66%. The piecewise model can effectively improve the anti-saturation ability of the spectral indices. We suggest a rapid and accurate approach for monitoring the CRC and provide a more suitable CRC mapping strategy for high-density areas of crop residue using multispectral remote sensing data.

A spectral analysis of stem bark for boreal and temperate tree species.

The woody material of forest canopies has a significant effect on the total forest reflectance and on the interpretation of remotely sensed data, yet research on the spectral properties of bark has been limited. We developed a novel measurement setup for acquiring stem bark reflectance spectra in field conditions, using a mobile hyperspectral camera. The setup was used for stem bark reflectance measurements of ten boreal and temperate tree species in the visible (VIS) to near‐infrared (NIR) (400–1000 nm) wavelength region. Twenty trees of each species were measured, constituting a total of 200 hyperspectral reflectance images. The mean bark spectra of species were similar in the VIS region, and the interspecific variation was largest in the NIR region. The intraspecific variation of bark spectra was high for all studied species from the VIS to the NIR region. The spectral similarity of our study species did not correspond to the general phylogenetic lineages. The hyperspectral reflectance images revealed that the distributions of per‐pixel reflectance values within images were species‐specific. The spectral library collected in this study contributes toward building a comprehensive understanding of the spectral diversity of forests needed not only in remote sensing applications but also in, for example, biodiversity or land surface modeling studies.

Sugariness prediction of Syzygium samarangense using convolutional learning of hyperspectral images

Sugariness is one of the most important indicators to measure the quality of Syzygium samarangense, which is also known as the wax apple. In general, farmers used to measure sugariness by testing the extracted juice of the wax apple products. Such a destructive way to measure sugariness is not only labor-consuming but also wasting products. Therefore, non-destructive and quick techniques for measuring sugariness would be significant for wax apple supply chains. Traditionally, the non-destructive method to predict the sugariness or the other indicators of the fruits was based on the reflectance spectra or Hyperspectral Images (HSIs) using linear regression such as Multi-Linear Regression (MLR), Principal Component Regression (PCR), and Partial Least Square Regression (PLSR), etc. However, these regression methods are usually too simple to precisely estimate the complicated mapping between the reflectance spectra or HSIs and the sugariness. This study presents the deep learning methods for sugariness prediction using the reflectance spectra or HSIs from the bottom of the wax apple. A non-destructive imaging system fabricated with two spectrum sensors and light sources is implemented to acquire the visible and infrared lights with a range of wavelengths. In particular, a specialized Convolutional Neural Network (CNN) with hyperspectral imaging is proposed by investigating the effect of different wavelength bands for sugariness prediction. Rather than extracting spatial features, the proposed CNN model was designed to extract spectral features of HSIs. In the experiments, the ground-truth value of sugariness is obtained from a commercial refractometer. The experimental results show that using the whole band range between 400 and 1700 nm achieves the best performance in terms of °Brix error. CNN models attain the °Brix error of ± 0.552, smaller than ± 0.597 using Feedforward Neural Network (FNN). Significantly, the CNN’s test results show that the minor error in the interval 0 to 10°Brix and 10 to 11°Brix are ± 0.551 and ± 0.408, these results indicate that the model would have the capability to predict if sugariness is below 10°Brix or not, which would be similar to the human tongue. These results are much better than ± 1.441 and ± 1.379 by using PCR and PLSR, respectively. Moreover, this study provides the test error in each °Brix interval within one Brix, and the results show that the test error is varied considerably within different °Brix intervals, especially on PCR and PLSR. On the other hand, FNN and CNN obtain robust results in terms of test error.

Colour constancy failures expected in colourful environments

Colour constancy refers to the constant perceived or apparent colour of a surface despite changes in illumination spectrum. Laboratory measurements have often found it imperfect. The aim here was to estimate the frequency of constancy failures in natural outdoor environments and relate them to colorimetric surface properties. A computational analysis was performed with 50 hyperspectral reflectance images of outdoor scenes undergoing simulated daylight changes. For a chromatically adapted observer, estimated colour appearance changed noticeably for at least 5% of the surface area in 60% of scenes, and at least 10% of the surface area in 44% of scenes. Somewhat higher frequencies were found for estimated changes in perceived colour relations represented by spatial ratios of cone-photoreceptor excitations. These estimated changes correlated with surface chroma and saturation. Outdoors, the colour constancy of some individual surfaces seems likely to fail, particularly if those surfaces are colourful.

Bloodstain Identification Based on Visible/Near-Infrared Hyperspectral Imaging With Convolutional Neural Network

Blood samples are easily damaged in traditional bloodstain detection and identification. In complex scenes with interfering objects, bloodstain identification may be inaccurate, with low detection rates and false-positive results. In order to meet these challenges, we propose a bloodstain detection and identification method based on hyperspectral imaging and mixed convolutional neural networks, which enables fast and efficient non-destructive identification of bloodstains. In this study, we apply visible/near-infrared reflectance hyperspectral imaging in the 380-1000 nm spectral region to analyze the shape, structure, and biochemical characteristics of bloodstains. Hyperspectral images of bloodstains on different substrates and six bloodstain analogs are experimentally obtained. The acquired spectral pixels are pre-processed by Principal Component Analysis (PCA). For bloodstains and different bloodstain analogs, regions of interest are selected from each substance to obtain pixels, which are further used in convolutional neural network (CNN) modeling. After the mixed CNN modeling is completed, pixels are selected from the hyperspectral images as a test set for bloodstains and bloodstain analogs. Finally, the bloodstain recognition ability of the mixed 2D-3D CNN model is evaluated by analyzing the kappa coefficient and classification accuracy. The experimental results show that the accuracy of the constructed CNN bloodstain identification model reaches 95.4%. Compared with other methods, the bloodstain identification method proposed in this study has higher efficiency and accuracy in complex scenes. The results of this study will provide a reference for the future development of the bloodstain online detection system.

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection.

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Effect of potassium supply on content of apple leaf phosphorus

Well-balanced mineral nutrition of apple trees is critical for fruit quality and storability. The seasonal changes of phosphorus and potassium leaf content were studied in the pot sand culture during the seasons of 2019 and 2020. The treatments comprised the application of the nutrient solution with different concentrations (0.00; 0.68; 1.36; 1.70; 2.04; 2.72; 3.40 g l-1). Leaves were analyzed on potassium and phosphorus content. The increase in potassium supply led to a striking increase in the content of apple leaf phosphorus above the optimal level. An approach to non-invasive detection of the impact of the nutrient imbalance based on hyperspectral reflectance imaging has been proposed.