Algorithms for detecting cherry pits on the basis of transmittance mode hyperspectral data

The suitability of the hyperspectral transmittance imaging technique was assessed in terms of detecting the internal intrusions (pits and their fragments) in cherries. Herein, hyperspectral transmission images were acquired in the visible and near-infrared range (450-1000 nm) from pitted and intact cherries of three popular cultivars: ‘Łutówka’, ‘Pandy 103’, and ‘Groniasta’, differing by soluble solid content. The hyperspectral transmittance data of fresh cherries were used to determine the influence of differing soluble solid content in fruit tissues on pit detection effectiveness. Models for predicting the soluble solid content of cherries were also developed. The principal component analysis and the second derivative pre-treatment of the hyperspectral data were used to construct the supervised classification models. In this study, five classifiers were tested for pit detection. From all the classifiers studied, the best prediction accuracies for the whole pit or pit fragment detection were obtained via the backpropagation neural networks model (87.6% of correctly classified instances for the training/test set and 81.4% for the validation set). The accuracy of distinguishing between drilled and intact cherries was close to 96%. These results showed that the hyperspectral transmittance imaging technique is feasible and useful for the non-destructive detection of pits in cherries

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period

Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria.

In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%)

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward

Disease incidence (a) and severity (b) of oilseed rape (<i>Brassica napus</i>) plants treated with four <i>Alternaria</i> species on various d after inoculation (dai).

<p>Disease incidence was calculated as the percentage of infected plants treated with <i>Alternaria</i> spp. Disease severity was measured according to a 0–9 scale, where 0 is a healthy plant and 9 is a plant completely damaged by the pathogen. <i>Alternaria</i> host species to oilseed rape: <i>A</i>. <i>brassicae</i>, <i>A</i>. <i>brassicicola</i>, <i>A</i>. <i>alternata</i>; non-host <i>Alternaria</i> species to oilseed rape: <i>A</i>. <i>dauci</i>.</p

Spectral characteristics of rape leaves infected with four species of <i>Alternaria</i> fungi during four studied periods.

<p>Spectral characteristics of rape leaves infected with four species of <i>Alternaria</i> fungi during four studied periods.</p

Confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied days after inoculation (dai).

<p>The upper number in each entry of the matrix is the average number of actual recognised classes in testing mode; the bottom one (its percentage) refers to the total number of cases used in testing (251). The diagonal entries of the matrix represent the mean quantities of the properly recognised cases (the upper value) and also their ratios with respect to the total representation of all testing data (the lower values are expressed as a percentage). Each entry outside the diagonal indicates an error (the number of misclassifications and its relative value). The last column of the matrix represents the total percentage measure of accuracy of actual recognition for the class indicated by the classifier. The upper number in this column represents the ratio of the number of the properly recognised cases to the total number of cases indicated by the particular output. The bottom numbers in the last column represent false alarm ratios. The last row of the matrix represents the ratios of the number of properly recognised cases to the total number of true cases (targets). The bottom numbers in this row are the misclassification ratios (the complement of sensitivity to one).</p

Box plots of temperature distributions during four studied periods after inoculation with various species of the genus <i>Alternaria</i>

<p><b>.</b> The boxes in this plot range from the 25–75^th percentile of the data (inter-quartile range). The horizontal line within the box is the median (50^th percentile of the data). The diamond within the box is the mean value. The whiskers extend 1.5 times the inter-quartile range. The dots on the whiskers are the outliers.</p

Density plots of leaf temperature distributions on succeeding days after inoculation for various types of inoculations

<p><b>.</b> A kernel density curve is shown estimating population distribution based on the sample data. The adjust parameter, which is responsible for the amount of smoothing, was set to 4.</p

The confusion matrix obtained with 10-fold cross-validation for the back-propagation neural network model for the studied <i>Alternaria</i> species.

<p>The parameters of the matrix are analogous to those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122913#pone.0122913.g008" target="_blank">Fig 8</a>.</p