Leaf Orientation Research Articles

Assessing grapevine water status through fusion of hyperspectral imaging and 3D point clouds

Mild to moderate and timely water deficit is desirable in grape production to optimize fruit quality for winemaking. It is crucial to develop robust and rapid approaches to assess grapevine water stress for scheduling deficit irrigation. Hyperspectral imaging (HSI) has the potential to detect changes in leaf water status, but the robustness and accuracy are restricted in field applications. The varied leaf orientations can significantly affect how light interacts with the plant, ultimately influencing the reflectance properties. This study focused on developing an approach for detecting grapevine water status using HSI and 3D data. Leaf orientation parameters derived from 3D point clouds were integrated with spectral signatures to address the spectral variance caused by variations in leaf orientation. A water status assessment model was developed based on multiblock partial least squares (MBPLS) to estimate midday leaf water potential (ΨL) using spectral signatures and leaf orientation parameters. HSI and 3D point clouds of selected leaves were captured simultaneously in a vineyard during the 2021 growing season, and ΨL was measured as the ground truth to assess the model performance. Mean spectral reflectance was derived from the hyperspectral images, while leaf orientation parameters were extracted from 3D point cloud data. The dataset was split randomly into 70% training/calibration and 30% test datasets. The test result shows that the model estimated the ΨL with R2 = 0.89, RMSE=0.12 MPa and MAE=0.09 MPa. The leaf orientation parameters derived from 3D point clouds had a contribution of 6.25% in estimating ΨL. Moreover, it acted as an enhancing component that explained the spectral variance caused by variations in leaf orientation and improved the interpretation of the underlying relationship between spectral reflectance and vine water status.

Characterization of Decellularized Plant Leaf as an Emerging Biomaterial Platform.

Decellularized plants have emerged as promising biomaterials for cell culture and tissue engineering applications due to their distinct material characteristics. This study explores the biochemical, mechanical, and structural properties of decellularized leaves that make them useful as biomaterials for cell culture. Five monocot leaf species were decellularized via alkali treatment, resulting in the effective removal of DNA and proteins. The Van Soest method was used to quantitatively evaluate the changes in cellulose, hemicellulose, and lignin content during decellularization. Tensile tests revealed considerable variations in mechanical strength depending on the plant species, the decellularization state, and the direction of applied mechanical force. Decellularized monocot leaves exhibited a notable reduction in mechanical strength and anisotropic properties depending on the leaf orientation. Imaging revealed inherent microgrooves on the epidermis of the monocot leaves. Permeability studies, including water uptake and biomolecule transport through decellularized leaves, confirmed excellent water uptake capability but limited biomolecule transport. Lastly, the plants were enzymatically degradable using typical plant enzymes, which were minimally cytotoxic to mammalian cells. Taken together, the features of decellularized plant leaves characterized in this study suggest ways in which they can be useful in cell culture and tissue engineering applications.

Geometric Fidelity Requirements for Meshes in Automotive Lidar Simulation

The perception of vegetation is a critical aspect of off-road autonomous navigation, and consequentially a critical aspect of the simulation of autonomous ground vehicles (AGVs). Representing vegetation with triangular meshes requires detailed geometric modeling that captures the intricacies of small branches and leaves. In this work, we propose to answer the question, “What degree of geometric fidelity is required to realistically simulate lidar in AGV simulations?” To answer this question, in this work we present an analysis that determines the required geometric fidelity of digital scenes and assets used in the simulation of AGVs. Focusing on vegetation, we use a comparison of the real and simulated perceived distribution of leaf orientation angles in lidar point clouds to determine the number of triangles required to reliably reproduce realistic results. By comparing real lidar scans of vegetation to simulated lidar scans of vegetation with a variety of geometric fidelities, we find that digital tree models (meshes) need to have a minimum triangle density of >1600 triangles per cubic meter in order to accurately reproduce the geometric properties of lidar scans of real vegetation, with a recommended triangle density of 11,000 triangles per cubic meter for best performance. Furthermore, by comparing these experiments to past work investigating the same question for cameras, we develop a general “rule-of-thumb” for vegetation mesh fidelity in AGV sensor simulation.

Non-contact leaf wetness measurement with laser-induced light reflection and RGB imaging

Leaf wetness duration is a crucial factor in plant disease management. Current optical methods use standard RGB images to classify leaf wetness as a binary problem, i.e., wet or dry. Green leaves absorb red light, whereas water reflects it. Based on this difference, an experimental platform was built to semi-automatically measure droplet deposition on grape leaves while capturing red laser images using an RGB camera. The setup measured changes in leaf mass and area of scanned leaves to determine the water mass per leaf area as a measure of leaf wetness. A sprayer was used to apply water droplets to the leaves. As the amount of deposited water increased, the mean red channel intensity decreased, with more bright spots in the images. These bright spots were more distinguishable as droplets in the green channel. Segmented leaf area, mean red channel intensity, and the number of identified droplets were used as image features. A generalised additive model was employed to predict the leaf wetness value with extracted features. The R-squared value for the prediction of the validation dataset was 0.71. Image resolution and leaf orientation were identified as factors that influenced the model accuracy. The measurement method introduced in this study shows potential for accurately quantifying leaf wetness, and implies that in practice detecting leaf wetness can be integrated into a multi-classification problem, thereby broadening the potential applications of optical methods.

Building a reduced order model from CFD data on leaves to evaluate optimal climate conditions

Climate control in vertical farms or greenhouses is crucial in order to have the optimal climate for the plants. This climate can be simulated using Computational Fluid Dynamics (CFD). This way, different ventilation methods can for example be compared. Previously, plants were modeled as a porous zone, in which additional source terms are added to the energy or transpiration transport equation. In this study, ten different leaves under different orientations are studied. The heat and mass balance for each leaf is solved and the boundary layer is adequately resolved. Different leaf orientations and positions to the flow are used, so that each leaf has its own distinct boundary layer. Leaf temperature and humidity are a function of the incoming flow, but also of the incoming temperature, humidity and stomatal resistance. In order to be able to quickly assess the effects of changing boundary conditions, on the temperature and humidity of the leaves, a Reduced Order Model (ROM) is made from the CFD-data. This method allows to quickly assess the effects of a stomatal resistance, the incoming radiation or a changing inlet temperature or humidity. A ROM was built using the data from one simulation. In order to see whether the ROM is able to predict other configurations, two additional cases were solved in CFD and were compared to the ROM. Leaf temperatures and humidities only differed maximally 3.14% and were quite good at predicting this. Predicted leaf transpiration rates differed slightly more, as these differed maximally 10.5%. Overall, the ROM has proven to be a valuable and numerically cost-effective tool to predict overall leaf climate and can be a good tool when later the climate of a full plant is simulated.

Identification of superior genotypes for leaf architecture traits in Sorghum bicolor through GGE biplot analysis

Context Well-organised leaf architecture produces compact canopies and allows for greater sunlight penetration, higher photosynthetic rates, and thus greater yields. Breeding for enhanced leaf architecture of sorghum (Sorghum bicolor L.), a key food source in semi-arid regions, benefits its overall production. Aims The study focuses on selecting useful genotypes with excellent leaf architecture for grain sorghum improvement. Methods In total, 185 sorghum genotypes were subjected to multi-environment trials. Leaf flagging-point length, leaf length, leaf width, leaf angle and leaf orientation value (LOV) were characterised under field conditions. Genotype + genotype × environment interaction (GGE) biplot analysis was used to identify the most stable genotypes with the highest LOV. Key results Statistical analysis showed significant effects of genotype × environment interaction (P < 0.001), and high broad-sense heritability for the traits. Correlation analysis demonstrated negative correlations (P < 0.001) between LOV and its components. Singular value decomposition of LOVs in the first two principal components explained 89.19% of the total variation. GGE biplot analysis identified G55 as the ideotype with the highest and most stable LOV. Conclusions Leaf architecture optimisation should be given greater attention. This study has identified a genotype with optimal and stable leaf architecture, laying the foundation for improvement in breeding to increase overall yields of sorghum. Implications Genotype G55 can be utilised as a parent with other parents that display economically important characteristics in breeding programs to produce offspring that can be planted densely to increase population yields. Genotypes identified with loose leaf architecture are useful in dissecting genes controlling leaf architecture by crossing with G55 to construct genetic mapping populations.

Hardware Development for Plant Cultivation Allowing In Situ Fluorescence Analysis of Calcium Fluxes in Plant Roots Under Microgravity and Ground-Control Conditions.

Maintaining an optimal leaf and stem orientation to yield a maximum photosynthetic output is accomplished by terrestrial plants using sophisticated mechanisms to balance their orientation relative to the Earth's gravity vector and the direction of sunlight. Knowledge of the signal transduction chains of both gravity and light perception and how they influence each other is essential for understanding plant development on Earth and plant cultivation in space environments. However, in situ analyses of cellular signal transduction processes in weightlessness, such as live cell imaging of signaling molecules using confocal fluorescence microscopy, require an adapted experimental setup that meets the special requirements of a microgravity environment. In addition, investigations under prolonged microgravity conditions require extensive resources, are rarely accessible, and do not allow for immediate sample preparation for the actual microscopic analysis. Therefore, supply concepts are needed that ensure both the viability of the contained plants over a longer period of time and an unhindered microscopic analysis in microgravity. Here, we present a customized supply unit specifically designed to study gravity-induced Ca2+ mobilization in roots of Arabidopsis thaliana. The unit can be employed for ground-based experiments, in parabolic flights, on sounding rockets, and probably also aboard the International Space Station.

Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

Modelling Canopy Temperature of Crops With Heterogeneous Canopies Grown Under Solar Panels

With global warming and the increase of heatwaves frequencies, it has become urgent to protect crops. Agrivoltaic systems tackle this issue by shading plants with photovoltaic panels to lower the temperature of canopies. However, a permanent shading would lead to an important loss of carbon for plants. For this reason, dynamic agrivoltaic systems (AVD) emerged with panels which could be steered in real time according to the needs of plants. Shading at the right time is not that easy with the risk to either miss a hot event and cause serious and irreversible injuries to the plants or shade too often, and impact carbon production. In this paper we present first an experiment with measurements of leaf temperature at different positions of grapevine canopy for two summer days in 2020 and 2021. Then, the energy balance sub-model part of a crop model that simulate plant growth for fruit trees and vines grown in heterogeneous AVD environments is presented. Finally, after having evaluated the coherence of the model with experimental results, the relevance of a mechanistic model to steer solar panels and protect plants from heat is illustrated through several examples. The heterogeneity of temperature within the canopy observed in the field experiments related with different variables such as air and ground temperature, leaf orientation and self-shading was correctly reproduced by the model. This work indicated that canopy temperature could be more integrative than a unique threshold of air temperature to take decisions on panel orientation to protect plants from heat stress.

Characterization of Coloured Sorghum Genotypes for Qualitative and Quantitative Characters

The experiment was conducted at College of Agriculture, Raichur, University of Agricultural Sciences, Raichur, Karnataka, India during October 2021–Februrary 2022 to characterize the 100-coloured sorghum genotypes along with four checks viz., M 35-1, AKJ 1, Paiyur 2 and GS-23 for eight qualitative characters and 10 quantitative characters in an augmented block design. The results revealed that, majority of the genotypes exhibited the pigmented stem (93%) and leaves almost green (61%) at harvest, horizontal flag leaf orientation (100%), loose erect primary branches of panicle (38%), black glumes (58%), red grain colour (36%), circular grain shape (75%) and freely threshable (93%) characters. For all the quantitative characters studied, a wide range of variation was observed. For stay green character, genotype IS 21868 showed completely green leaves. Similarly, for other characters also superior genotypes were identified in this study. Such genotypes can be used further in improvement of sorghum for its grain yield. For grain yield, 14 genotypes exhibited higher yields when compared to the best check GS-23, viz., IS 29032 (120.0 g), IS 6508 (119.18 g), IS 16006 (113.80 g), IS 28065 (102.88 g), IS 23890 (94.88 g), IS 28049 (94.28 g), IS 23865 (89.48 g), IS 29031 (87.98), IS 28202 (87.88), IS 31706 (86.48), IS 2582 (85.88 g), IS 28200 (83.58), IS 16398 (81.08 g) and IS 23955 (80.0 g). Eight genotypes were on par with GS-23. These genotypes can be further evaluated for its yield stability within and across locations.

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Processing Plant Diseases Using Transformer Model

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.

Enhancing maize radiation use efficiency under high planting density by shaping canopy architecture with a plant growth regulator

Optimized maize (Zea mays L.) canopy architecture enhances density-tolerance. DHEAP (N, N-Diethyl-2-hexanoyl oxygen radicals-ethyl amine (2-ethyl chloride) phosphonic acid salt) has been shown to increase maize upper canopy strata compactness, but its overall effect on the whole canopy structure and how it shapes the canopy structure remain unclear. This study examined how DHEAP affected the canopy structure of maize hybrids Zhengdan 958 (ZD958) and Xianyu 335 (XY335), with distinct canopy structures, under different planting densities. The results showed that DHEAP increased the leaf orientation value (LOV) of upper canopy strata by 8.0% while reducing middle and lower strata LOV by 11.7% and 18.4%, respectively. This indicates that DHEAP shaped a canopy structure that was compact in the upper strata and loose in the middle and lower strata. Multiple linear regression analysis showed that leaf angle had a greater impact on the upper canopy strata, while leaf auricle size had a greater impact on the middle and lower canopy strata. After DHEAP treatment, light transmission above different canopy strata increased at the reproductive stage. Concurrently, the middle canopy captured more light energy, enhanced yield formation, and boosted radiation use efficiency by 21.9% under high density. In terms of grain yield, DHEAP treatment resulted in a 9.1% and 23.9% increase in ZD958 and XY335, respectively, under high-density conditions. These results suggest that DHEAP shaped the maize canopy structure with high density tolerance, improved the distribution of light within the canopy, and increased grain yield.

Comparative effects of drought stress on leaf gas exchange, foliar ABA and leaf orientation in four grapevine cultivars grown in Northern Italy.

Drought tolerance varies greatly across Vitis vinifera cultivars, depending on physiological responses and structural and morphological adaptations. In this study, responses to water stress were examined in three extensively cultivated varieties from Northern Italy. Over the course of two seasons, mature potted vines were subjected to a 12 or 13-day period of water restriction. Vine water relations were investigated using measures of water potential, gas exchanges, and leaf ABA content. Leaf angle response to increasing water stress was analysed in the four cultivars as a mechanism that improves stress tolerance. Different physiological responses were observed among cultivars, suggesting a near-isohydric water-use strategy for Moscato and a near-anisohydric one for Garganega, Glera and Merlot. Results of leaf ABA analysis highlighted a variability among the studied varieties, indicating higher contents and lower sensitivity to ABA for the anisohydric ones. In all varieties, a similar increase in midday leaf inclination was observed in response to decreasing stem water potentials, indicating that leaf angle adjustments may represent a common adaptive response to drought. These findings increase the understanding of the leaf physiological and structural mechanisms that contribute to water stress tolerance in grapevine, supporting a more efficient cultivar selection to cope with the expected changes in Mediterranean climate.

Upscaling plot-based measurements of RUSLE C-factor of different leaf-angled crops in semi-arid agroecosystems.

Several models have been used to assess temporal cover change trends by using remote and proximal sensing tools. Particularly, from the point of hydrologic and erosional processes and sustainable land and soil management, it is crucial to determine and understand the variation of protective canopy cover change within a development period. Concordantly, leaf angle distribution (LAD) is a crucial parameter when using the vegetation indices (VIs) to define the radiation reflected by the canopy when estimating the cover-management factor (C-factor). This research aims to assess the C-factor of cultivated lands with sunflower and wheat that have different leaf orientations (planophile and erectophile, respectively) with the help of reduced models of NDVI and LAI for estimating crop-stage SLR values with the help of a stepwise linear regression. Those equations with R-squared values of 0.85 and 0.93 were obtained for sunflower and wheat-planted areas, respectively. The Normalized Difference Vegetation Index (NDVI), one of the two plant indices used in this study, was measured by remote and proximal sensing tools. At the same time, the Leaf Area Index (LAI) was obtained by a proximal hand-held crop sensor alone. Soil loss ratio (SLR) was upscaled for the establishment period (1P) of sunflower and the maturing period (3P) of wheat to present different growth stages simultaneously with plant-specific equations that can be easily adapted to those aforementioned crops instead of doing field measurements with conventional techniques in semi-arid cropping systems.

Morphological characteristics and variability of traditional starch forming coix (Coix lacryma-jobi var. ma-yuen) populations from Mindanao Island, Philippines

Abstract. Bon SG, Antesco GP, Enicola EE. 2023. Morphological characteristics and variability of traditional starch forming coix (Coix lacryma-jobi var. ma-yuen populations from Mindanao Island, Philippines. Biodiversitas 24: 4382-4391. Adlay (Coix lacryma-jobi var. ma-yuen (Rom.Caill.) Stapf) is an underutilized crop in the Philippines with high potential. Characterization and elucidation of the available variability among the local adlay populations are essential information for its improvement and better utilization. The study aimed to characterize the qualitative morphological traits of 34 traditional adlay populations from Mindanao Provinces and assess diversity by the Shannon Index and similarity by multivariate clustering. Results showed that all populations shared the same morphotypes in five descriptors but were variable for the rest. Six populations showed intense leaf anthocyanin coloration, and 23 expressed upright growth habits. Three types of leaf blade colors were observed, while the intensity of anthocyanin coloration of culms was expressed in four types. Most populations had semi-pubescent or highly pubescent leaves, generally intermediate leaf orientation and non-glaucous leaves. Immature grains were generally light green but expressed four color types at maturity, mostly circular with furrows on the surface. Diversity Indices (H') ranged from low to high. Leaf blade pubescence, anthocyanin coloration of culm, and mature grain color had high H' values. Cluster analysis showed that the variability was narrow, where overall clustering achieved 0.50 similarity. At 0.65 similarity, three groups can be derived with three populations as outliers. Clustering can be attributed to the differences in seedling color, anthocyanin coloration, plant growth habit, immature grain color, mature grain color, and shape. The study established the morphological description of the qualitative traits of traditional Philippine adlay and assessed diversity to be generally intermediate, where populations can be clustered into subgroups according to the degree of morphotype similarities. Clustering, however, was not related to provenances. It is recommended to conduct further geographic germplasm sampling, marker-based characterization, and agronomic evaluation to dissect further the genetic diversity and crop potential of the local adlay populations.

Enhancing maize yield through understanding the novel shoot-root interaction in morphology among hybrids with dense planting

ContextUnderstanding the shoot-root characteristics and their interaction of maize is essential to break the constraints of the densification process and improve grain yield. ObjectiveThe objective of the work was to study the interaction between the above-ground canopy and below-ground root system in morphology and its relationship to grain yield in dense planting with different hybrids. MethodsThis study conducted a 2-yr field experiment with three modern hybrids (JNK728, ZD958 and XY335) at the Wuqiao Experimental Station in Northern China. ResultsAmong hybrids, XY335 attained the highest grain yield of 14,886.9 kg hm−2 in 2019 and 10,078.8 kg hm−2 in 2020 with a density of 7.5 × 104 plants hm−2. For the shoot at the silking stage (R1), the lowest leaf orientation value for both middle and upper leaves and ear-to-height ratio among three hybrids were found in XY335 with the compact plant phenotype. Meanwhile, the highest photosynthesis rate of ear leaves (33.0 umol m2˙s−1) at R1 was also observed in XY335. At physiological maturity (R6), the above-ground dry matter (AGDM) was 4.6%− 10.4% higher in XY335 than the other two hybrids. For the root system at both R1 and R6 with the density of 7.5 × 104 plants hm−2, the highest value for root system indicators in morphology such as root total length (RL), root surface area (RSA), root volume (RV) was observed in XY335. From R1 to R6, RL, RSA and RV in XY335 averaged 24,749.6 cm, 3486.8 cm2, 51.5 cm3, 1.3–14.6% higher than JNK728, and 10.2–23.9% higher than ZD958, respectively. Furthermore, the lower senescence of the root system was also observed in XY335, with a 22.0–32.1% lower decrease from R1 to R6 in RL, RSA, and RV compared with the other two hybrids. ConclusionsFor the shoot-root interaction, per unit decrease in morphology indicator in the root per plant with density increase of XY335 accompanied with more AGDM accumulation and thus higher grain yield. ImplicationsThe observations on shoot-root characteristics and their interaction from this study are essential to selecting a hybrid that allows a better adaptation to planting at high density, developing an effective management strategy, and breeding high-density tolerant hybrids.

Multi-locus genome-wide association studies reveal genetic loci and candidate genes for leaf angle traits in cut chrysanthemum

Chrysanthemum (Chrysanthemum morifolium Ramat.), one of the four most popular cut flowers in the world, occupies a large share of global floriculture trade. Leaf angle (LA) is an important factor affecting plant architecture, photosynthesis and yields of cut chrysanthemum. However, the genetic basis and related genes underlying LA remain largely unknown. In this research, phenotypes of five LA-related traits in 200 cut chrysanthemum accessions were investigated across two consecutive years (Y2020 and Y2021), and their broad-sense genetic heritabilities ranged from 68.91% to 84.70%. Leaf orientation value (LOV) exhibited the maximum phenotypic coefficient of variation of 35.66% in Y2020 and 32.34% in Y2021. A new methodological framework named 3VmrMLM was applied for multi-locus genome-wide association study (GWAS) based on a set of 330,710 single nucleotide polymorphisms (SNPs) to explore the genetic loci and candidate genes of LA. As a result, 303 quantitative trait nucleotides (QTNs) in single-environment, 120 QTNs and 75 QTN × environments interactions (QEIs) in multiple-environments were identified. Among the genes around these QTNs and QEIs, 26 known genes were considered to be associated with LA, including ARF16, IBH1, AN3, etc. Furthermore, the haplotype analysis of ARF16 revealed four accessions with elite LA. Our findings provide a fundamental understanding to the genetic architecture of LA and contribute to further gene cloning and molecular improvement of chrysanthemum plant architecture.

Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A

In order to evaluate forest quality and carbon stocks and improve our understanding of ecosystems and carbon cycling processes, the accurate measurement of aboveground biomass (AGB) and other forest characteristics is crucial. This paper considers the response differences between the bands obtained from Landsat 8 and Sentinel-2A sensors, respectively, and combines the exhaustive combination of spectral indices with normalization and ratio techniques to establish suitable weights for the bands in the vegetation index using relative sensitivity and noise equivalent (NE) to improve the saturation effect between the vegetation index and forest parameters (canopy closure (CC), forest stand density (S), basal area (BA), and AGB) and extend the linear relationship between them. This paper also considers the effects of window size, direction, and principal component analysis on texture features, adds weight to textures and combines textures using linear correlation and NE, establishes texture indices to improve the limitations of information contained in individual texture features, analyzes the potential of texture features to evaluate each forest parameter under different conditions, and better captures the variation of forest parameters. In this paper, we only analyze the planted coniferous forest in Saihanba to avoid the differences in electromagnetic wave effects that are difficult to judge and analyze because of the differences in leaf size and leaf orientation between coniferous and broad-leaf forests. In contrast, the vegetation indices and texture indices obtained from Sentinel-2A could better estimate each vegetation parameter, and the linear estimation of each vegetation parameter using the new texture index reached an R2 above 0.65. The results of this study indicate that Sentinel-2A and Landsat 8 are promising remote sensing datasets for estimating vegetation parameters at the regional scale, and Sentinel-2A data can be employed as the primary source of earth observation data for assessing forest resources in the Saihanba area.