Search Results (9)

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization. Full article

Let $\mathcal{B}{T}_{4l}$ denote a subclass of bounded turning functions connected with a four-leaf-type domain. The goal of the study is to probe into the bounds of coefficients $|b_6|,|b_7|,|b_8|$, the bounds of the logarithmic coefficients, and the third-order determinants $|H_{3,1}|,|H_{3,2}|,|H_{3,3}|$ for the functions in this class. Full article

Key leaf functional traits, such as chlorophyll and carotenoids content (C_ab and C_xc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm⁻²; 1.3 µg·cm⁻²; 0.0009 cm; and 0.0009 g·cm⁻² for C_ab, C_xc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for C_ab, C_xc, and EWT (RMSE = 7.8 µg·cm⁻²; 2.0 µg·cm⁻²; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm⁻²) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm⁻² respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons. Full article

GATA transcription factors are crucial proteins in regulating transcription and are characterized by a type-IV zinc finger DNA-binding domain. They play a significant role in the growth and development of plants. While the GATA family gene has been identified in several plant species, it has not yet been reported in Phoebe bournei. In this study, 22 GATA family genes were identified from the P. bournei genome, and their physicochemical properties, chromosomal distribution, subcellular localization, phylogenetic tree, conserved motif, gene structure, cis-regulatory elements in promoters, and expression in plant tissues were analyzed. Phylogenetic analysis showed that the PbGATAs were clearly divided into four subfamilies. They are unequally distributed across 11 out of 12 chromosomes, except chromosome 9. Promoter cis-elements are mostly involved in environmental stress and hormonal regulation. Further studies showed that PbGATA11 was localized to chloroplasts and expressed in five tissues, including the root bark, root xylem, stem bark, stem xylem, and leaf, which means that PbGATA11 may have a potential role in the regulation of chlorophyll synthesis. Finally, the expression profiles of four representative genes, PbGATA5, PbGATA12, PbGATA16, and PbGATA22, under drought, salinity, and temperature stress, were detected by qRT-PCR. The results showed that PbGATA5, PbGATA22, and PbGATA16 were significantly expressed under drought stress. PbGATA12 and PbGATA22 were significantly expressed after 8 h of low-temperature stress at 10 °C. This study concludes that the growth and development of the PbGATA family gene in P. bournei in coping with adversity stress are crucial. This study provides new ideas for studying the evolution of GATAs, provides useful information for future functional analysis of PbGATA genes, and helps better understand the abiotic stress response of P. bournei. Full article

The theory of univalent functions has shown strong significance in the field of mathematics. It is such a vast and fully applied topic that its applications exist in nearly every field of applied sciences such as nonlinear integrable system theory, fluid dynamics, modern mathematical physics, the theory of partial differential equations, engineering, and electronics. In our present investigation, two subfamilies of starlike and bounded turning functions associated with a three-leaf-shaped domain were considered. These classes are denoted by ${\mathcal{BT}}_{3l}$ and ${\mathcal{S}}_{3l}^{*}$, respectively. For the class ${\mathcal{BT}}_{3l}$, we study various coefficient type problems such as the first four initial coefficients, the Fekete–Szegö and Zalcman type inequalities and the third-order Hankel determinant. Furthermore, the existing third-order Hankel determinant bounds for the second class will be improved here. All the results that we are going to prove are sharp. Full article

Dehydration proteins (dehydrins, DHNs) confer tolerance to water-stress deficit in plants. We performed a comparative genomics and evolutionary study of DHN genes in four model Brachypodium grass species. Due to limited knowledge on dehydrin expression under water deprivation stress in Brachypodium, we also performed a drought-induced gene expression analysis in 32 ecotypes of the genus’ flagship species B. distachyon showing different hydric requirements. Genomic sequence analysis detected 10 types of dehydrin genes (Bdhn) across the Brachypodium species. Domain and conserved motif contents of peptides encoded by Bdhn genes revealed eight protein architectures. Bdhn genes were spread across several chromosomes. Selection analysis indicated that all the Bdhn genes were constrained by purifying selection. Three upstream cis-regulatory motifs (BES1, MYB124, ZAT) were detected in several Bdhn genes. Gene expression analysis demonstrated that only four Bdhn1-Bdhn2, Bdhn3, and Bdhn7 genes, orthologs of wheat, barley, rice, sorghum, and maize genes, were expressed in mature leaves of B. distachyon and that all of them were more highly expressed in plants under drought conditions. Brachypodium dehydrin expression was significantly correlated with drought-response phenotypic traits (plant biomass, leaf carbon and proline contents and water use efficiency increases, and leaf water and nitrogen content decreases) being more pronounced in drought-tolerant ecotypes. Our results indicate that dehydrin type and regulation could be a key factor determining the acquisition of water-stress tolerance in grasses. Full article

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R² above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R² = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI₅₇₀), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI₁₆₄₀, PRI₅₇₀, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R² above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types. Full article

In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to reliably detect and track individual leaves of a maize plant based on a graph theoretic approach for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking, and generation of a leaf status report. The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant’s growth, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf ceased to grow, and the length and relative growth rate of individual leaves. Based on experimental study, three types of leaf intersections are identified, i.e., tip-contact, tangential-contact, and crossover, which pose challenges to accurate leaf tracking in the late vegetative stage. Thus, we introduce a novel curve tracing approach based on an angular consistency check to address the challenges due to intersecting leaves for improved performance. The proposed method shows high accuracy in detecting leaves and tracking them through the vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset. Full article

Metacaspases (MCs) are cysteine proteases that are important for programmed cell death (PCD) in plants. In this study, we identified 89 MC genes in the genomes of four Gossypium species (Gossypium raimondii, Gossypium barbadense, Gossypium hirsutum, and Gossypium arboreum), and classified them as type-I or type-II genes. All of the type-I and type-II MC genes contain a sequence encoding the peptidase C14 domain. During developmentally regulated PCD, type-II MC genes may play an important role related to fiber elongation, while type-I genes may affect the thickening of the secondary wall. Additionally, 13 genes were observed to be differentially expressed between two cotton lines with differing fiber strengths, and four genes (GhMC02, GhMC04, GhMC07, and GhMC08) were predominantly expressed in cotton fibers at 5–30 days post-anthesis (DPA). During environmentally induced PCD, the expression levels of four genes were affected in the root, stem, and leaf tissues within 6 h of an abiotic stress treatment. In general, the MC gene family affects the development of cotton fibers, including fiber elongation and fiber thickening while four prominent fiber- expressed genes were identified. The effects of the abiotic stress and hormone treatments imply that the cotton MC gene family may be important for fiber development. The data presented herein may form the foundation for future investigations of the MC gene family in Gossypium species. Full article