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The present work investigates the dependences between weather conditions and the specific characteristics of nine wheat varieties and the reflection of light spectra. The obtained images captured with an unmanned aerial vehicle UA V model DJI Mavic 2 Pro and RGN camera reflect the state of vegetation of wheat varieties depending on environmental factors and the specific morphological features of each variety. The differences are analyzed and presented by variety, highlighting the relationship between the factors through reflected light and the condition of the investigated winter wheat varieties. The study provides valuable information for future research on the influence of meteorological conditions on the accuracy of the results obtained with UAV imaging. [ABSTRACT FROM AUTHOR]

Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the heavy metals concentrations in soil, inflorescences of Brassica napus L. and rapeseed honey sampled from some regions of north-eastern Bulgaria. Thirteen locations were selected for experimental studies. The soils, plants and honey samples were taken from conventional beekeeping areas away from major industrial pollutants. The median amounts of elements in the soil samples (mg/kg dry weight (DW)) are 1.1246 for Fe(s), 0.7048 for Al(s), 0.5636 for Pb(s), 0.1658 for Cu(s) and 0.0148 for Zn(s). The median amounts of heavy metals measured in the inflorescences of Brassica napus (mg/kg DW) are 5.5430 for Fe(p), 2.9095 for Zn(p), 1.3225 for Pb(p), 0.2593 for Cu(p) and 0.2105 for Al(p). The median heavy metals concentrations in tested honey (mg/kg DW) are 1.0026 for Fe(h), 0.1849 for Al(h), 0.1832 for Pb(h), 0.1250 for Zn(h), and 0.0702 for Cu(h). The relationship between the heavy metal in soil, plants and honey was investigated using Spearman's rank correlation coefficient. Significant differences in the concentrations of Fe(s), Al(s) and Pb(s) in soils, Fe(p) and Zn(p) in the plant samples and Fe(h) in honey samples from the different locations were found. The heavy metal content tested in honey did not pose a risk to human health. [ABSTRACT FROM AUTHOR]