Forest soil CO2 efflux (FCO2) is a crucial process in global carbon cycling; however, how FCO2 respond to disturbance regimes in different forest biomes is poorly understood. We quantified the effects of disturbance regimes on FCO2 across boreal, temperate, tropical, and Mediterranean forests based on 1240 observations from 380 studies. Globally, FCO2 was increased by 13 to 25% due to climatic perturbations such as elevated CO2 concentration, warming, and increased precipitation. FCO2 was increased by forest conversion to grassland and elevated carbon input by forest management practices but was reduced by decreased carbon input, fire, and acid rain. Disturbance also caused changes in soil temperature and water content, which in turn affected the direction and magnitude of disturbance effects on FCO2. Our results suggest that disturbance effects on FCO2 should be incorporated into earth system models to improve the projection of feedback between the terrestrial C cycle and climate change.

Although how rare species persist in communities is a major ecological question, the critical phenotypic dimension of rarity is broadly overlooked. Recent work has shown that evaluating functional distinctiveness, the average trait distance of a species to other species in a community, offers essential insights into biodiversity dynamics, ecosystem functioning, and biological conservation. However, the ecological mechanisms underlying the persistence of functionally distinct species are poorly understood. Here we propose a heterogeneous fitness landscape framework, whereby functional dimensions encompass peaks representing trait combinations that yield positive intrinsic growth rates in a community. We identify four fundamental causes leading to the persistence of functionally distinct species in a community. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (either positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and some guidelines to distinguish among them. In addition to these deterministic processes, we also explore how stochastic dispersal limitation can yield functional distinctiveness.

Published literature suggests that indigenous cultural practices, specifically traditional medicine, are commonplace among urban communities contrary to the general conception that such practices are associated to rural societies. We reviewed literature for records of herptiles sold by traditional health practitioners in urban South Africa, then used visual confirmation surveys, DNA barcoding, and folk taxonomy to identify the herptile species that were on sale. Additionally, interviews with 11 SePedi and IsiZulu speaking traditional health practitioners were used to document details of the collection and pricing of herptile specimens along with the practitioners’ views of current conservation measures aimed at traditional medicine markets. The herptile specimens sold by traditional health practitioners included endangered and non-native species. The absorbance ratios of DNA extracted from the tissue of herptiles used in traditional medicine were found to be unreliable predictors of whether those extractions would be suitable for downstream applications. From an initial set of 111 tissue samples, 81 sequencing reactions were successful and 55 of the obtained sequences had species level matches to COI reference sequences on the NCBI GenBank and/or BOLD databases. Molecular identification revealed that traditional health practitioners sometimes mislabel the species they use. The mixed methodology employed here is useful for conservation planning as it updates knowledge of animal use in indigenous remedies and can accurately identify species of high conservation priority. Furthermore, the study highlights the possibility of collaborative conservation planning with traditional health practitioners.

Sustainable education is an approach of aiming to establish the sustainability act in students, and communicate the values among schools, colleges and communities the values and motivations future-in one's own life, in their communities, and on a worldwide platform.Along with I.C.T. students, their fresher professionals also need an intelligent system that can understand each professional's learning needs and help them excel in their skills.

Mung bean (Vigna radiata (L.) Wilczek) is an important crop providing protein, fiber, carbohydrates, and minerals in Southeast Asia and Africa. Trifoliate leaves in mung beans are central to several plant processes like photosynthesis, light interception, early disease & pest warning signals, and overall canopy architecture. We sampled more than 5000 leaf images of the Iowa Mung bean diversity panel (IMDP) during the 2020 and 2021 growing seasons in a Randomized Complete Block Design. We recorded the phenotypic diversity, developed a regression model for the oval leaflet type, and conducted GWAS for the image extracted traits. The diversity in the morphology included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A universal regression model LA = b0 + b1L + b2W + b3L*W was the best at predicting the area of each ovate leaflet with an adjusted R2 of 0.97. The candidate genes Vradi01g07560, Vradi05g01240, Vradi02g05730, and Vradi03g00440 are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right) and would be suitable candidates for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker-aided selection methods for mung bean crop improvement.

Plant height is a critical indicator for monitoring plant growth status and productivity estimation. Accurate measurement of plant height through a high-throughput manner is crucial for precision agriculture and field-based plant phenotyping. Manually measuring plant height is time-consuming and labor-intensive, and it only provides the height information at each sampling point but cannot tell the detailed within-field spatial variations. LiDAR and digital imagery-based photogrammetry have been increasingly used in plant phenotyping in recent years thanks to the developments in Unmanned Aerial Vehicle (UAV) and sensor technology. LiDAR point clouds can be directly used for plant height extraction, digital imagery-based photogrammetric point clouds can also be used for derivation plant height. The goal of this study is to investigate the potential of UAV LiDAR and digital photogrammetry in measuring plant height of different crops at multiple growth stages. To this end, a high resolution 32 channel LiDAR and digital cameras mounted on DJI Matrice 600 Pro UAV were employed to collect data from agricultural fields in Missouri, USA. Canopy Surface Models (CSM) and Digital Terrian Models (DTM) are generated from LiDAR and digital Photogrammetry point clouds, respectively, then plant height is derived by subtracting DTM from CSM, the UAV-based plant height is compared against manually measured height to evaluate the accuracy and performance of LiDAR and digital photogrammetry technologies. This study proved that UAV-based LiDAR and digital photogrammetry are important tools in sustainable field management and high-throughput phenotyping.

Volume is an important phenotype and quality attribute of sweetpotato storage roots. Conventionally the volume of most agricultural products is measured by water displacement. This method, which requires submerging the products in a container of water and measuring the displacement of water in the container, is time-consuming and tedious. It would be beneficial for sweetpotato breeding programs and quality inspection if a rapid method is developed for measuring the volume of sweetpotatoes. This study is therefore to evaluate the feasibility of LiDAR (light detection and ranging) technology as a novel high-throughput approach to phenotyping and measurement of the volume of sweetpotatoes. LiDAR data will be acquired from sweetpotato storage roots using a consumer-grade sensor, Intel® RealSense™ L515, which is an RGB-D (red-green-blue-depth) camera. Ground-truth volume values will be obtained using the reference water displacement method. RGB images will be used to segment sweetpotatoes from background, and extract meaningful features (e.g., the major axis length and the center of mass), complement the point cloud data from depth images for volume estimation. The shape of the sweetpotatoes will be constructed by a series of three-dimensional coordinate points, the alpha shape method is to be used to envelop the boundary points of sweetpotatoes to obtain a non-convex body, and thereby the volume of the sweet potato will be calculated. The efficacy of the proposed method will be evaluated in terms of volume estimation accuracy.

Reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant varieties. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Machine learning (ML) methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)]. This study uses disease severity from both visual field ratings and ML-based (using images) severity ratings collected from 473 accessions. Images were processed through an ML framework that identified soybean leaflets with SDS symptoms, and then disease severity was quantified on those leaflets into few classes. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS, such as ss715584164 and ss715610404, or near the potentially novel candidate genes, such as ss715583703 and ss715615734. Within previously reported SDS quantitative trait loci there were significant SNPs from both visual rating and image-based ratings. The results of this study provide an exciting avenue for using ML to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field stress phenotyping.

Pores in the leaf epidermis called stomata allow plants to take up carbon dioxide for photosynthesis, but are also pathways for water vapor loss. New image acquisition and analysis methods are allowing high-throughput phenotyping of stomatal patterning, which can be applied to better understand the genetic basis of variation in certain species. However, it takes considerable data and effort to train the models and their ability to accurately detect epidermal structures is constrained by the training data. This issue of context dependency, the inability to perform effectively in novel contexts, is the main hurdle preventing widespread adoption of machine learning in high-throughput phenotyping of intraspecific, interspecific, and environmental variation. Here we show the limited ability of a Mask-RCNN tool trained and successfully applied to Zea mays, to analyze images from a closely related grass called Setaria viridis. We then demonstrate successful retraining of the tool to cope with the novel amounts of diversity presented by this new species. The stomatal complexes in optical tomography images of mature Setaria leaves were accurately identified by comparison to expert raters (R 2 = 0.84). This study highlights the challenge of context dependency for widespread application of machine learning tools for phenotyping plant traits, even in closely related species. At the same time, it also provides a new tool that can be applied to leverage Setaria as a model C4 species, and a roadmap for the translation of a machine learning tool to analyze stomatal patterning in diverse datasets of new plant species.

ORCiD: [https://orcid.org/0000-0003-0655-2343] Increasing weather variability is affecting the overall productivity of agriculture. In this scenario, current crop improvement science is essential to improve productivity while retaining the quality of plant products. There has long been an interest in using process-based modeling to examine the interaction between environment and genotype. The methodological challenges to better predict how various environmental conditions may impact novel genotypes and it has been a fundamental barrier to model parameterization. Thus, a phenotypic campaign was conducted to collect a comprehensive physiological dataset from a panel of 25 genotypes (including both breeder panel and diversity panel) in the summer of 2022. Additionally, an unmanned aerial vehicle (UAV) was used to gather remote sensing data. The red-green-blue (RGB) 3D point cloud, NDVI (Normalize difference vegetation Indices), and LIDAR (Light detection and ranging) were also used to identify the trait variations among the genotypes. The data is being analyzed to explain the physiological and phenotypic trait differences. The outcome of this project would help to develop a genetically informed, realistic soybean model. Finally, it will help breeders and growers in locating high-yielding cultivars for the appropriate geographical areas.

Measurement of key crop physiological traits using high resolution aerial imagery with unmanned aerial systems (UASs) holds enormous potential to increase consistency and accuracy of data collected for field evaluations. Here, we demonstrate temporal corn response to fertility treatments using repeated measurements followed by an area-under-the-curve progression analysis. Radiometrically calibrated multispectral datasets were used to calculate standard vegetative indices as well as to leverage models that approximate leaf area, nitrogen content, chlorophyll content, and canopy uniformity. In addition, digital elevation models can be employed to measure relative canopy heights and spatial variability in the field. Taken together, these digital assessments allow for a researcher to have significant insight into experiment outcomes during the growing season, including the identification of relative yield potential. This approach automates and standardizes the acquisition of key phenotypes that can be used to more efficiently evaluate field trials across multi-location programs.

In recent decades, the field of phenomics has lagged behind the advances in genomics, which have become increasingly high-throughput and low-cost. In comparison, manually collected phenotypes are often time-consuming, labor intensive, and more costly to obtain. The development of high-throughput phenotyping platforms (HTPP) are bridging these gaps and enabling improved spatial and temporal resolution for researchers. We used imagery from unoccupied aerial vehicles (UAV) flown over multiple site years in Saskatchewan and Italy to gather data for crop height, area and volume in a lentil diversity panel. We found high correlations for our UAV-derived traits (height & volume) with our manually collected phenotypes (height & biomass). In addition, the high-throughput nature of the UAV allowed for the collection of time-series data which enabled the modelling of growth curves for volume, height and area, which would be impractical under traditional phenotyping procedures given the large population grown in multiple environments. Principal component analysis and hierarchical clustering revealed differential growth strategies amongst our diverse lentil population across contrasting environments. Our study demonstrates the potential for HTPP to obtain data that traditionally require destructive sampling, e.g., volume as a proxy for vegetative biomass, and improve the temporal quality of phenotype data enabling researchers to take their analysis beyond single time points, e.g., model growth curves. In addition, performing our analysis on data from contrasting environments, i.e., Saskatchewan and Italy, has helped elucidate optimal adaptation with regard to growth strategies in lentils.

Dani C. Gafford1, Jaime Barros1

Root studies in controlled environments are typically conducted either in rhizotrons, pots, or small scale mesocosm systems, like PVC tubes or root boxes. These systems have two limitations for translating results to crop roots grown in fields. First, the size and shape of containers change the root phenotype when plants are in the mature stage. Second, often only one plant is planted per container without interaction among neighboring plants. Therefore, the root architecture observed in these isolated environments has low predictability for the root architecture in a community setting in fields. To better translate the root traits observed in a controlled environment to field observations, we developed a macro-mesocosm system (5.5 m (W) x 6.7 m (L) x 0.7 m (H)) to mimic the real field soil conditions in a greenhouse. We also installed 64 capacitance soil moisture sensors to monitor the whole macro-mesocosm system at 15.24 cm and 38.10 cm soil depths in real-time. We evaluated the phenotypic spectrum in one common bean (Phaseolus vulgaris. L) genotype, SEQ7, in a time series experiment. We grew SEQ7 for two, six, nine, and twelve weeks under sensor-controlled water-stressed and well-watered irrigation regimes. SEQ7 showed four different root architecture types across developmental stages. These four root architecture types are consistent with previous field observation. This novel macro-mesocosm system will be a great setup to study the field dynamics of the root phenotypic spectrum in a controlled environment.

Stomata are the microscopic pores on plant leaves that open or close to regulate the flux of water from leaves. Guard cells of stomata are known to react to environmental conditions such as light and CO2 in order to optimize CO2 uptake and water loss. Stomatal anatomy (aperture, length, width, etc.) influences leaf-level physiology traits including conductance to water. Stomatal anatomy can be visualized in situ by microscopy, but the difficulty of regulating the atmospheric environment of a microscope stage means that the conditions under which imaging is done are rarely physiologically relevant. Alternatively, portable photosynthesis measuring instruments offer a non-destructive estimate of leaf gas exchange, including stomatal conductance, while the leaf experiences tightly controlled steady-state or dynamic environmental conditions. However, these measurements reflect stomatal characteristics in aggregate on a leaf area basis, which are heavily influenced by the mesophyll as well as epidermal structure and function. Observing the behavior of stomata by microscopy simultaneous to controlling the leaf environment and measuring gas exchange fluxes would allow advances in the understanding of leaf structure-function relationships. To reconcile the microscopic stomatal characteristics with leaf-level gas exchange we have combined laser scanning confocal microscopy and gas exchange instruments to simultaneously observe stomatal characteristics (e.g. stomatal aperture, pore depth, closing speed) and leaf-level traits like photosynthesis, transpiration, and stomatal conductance. Results are presented for the use of this approach on diverse plant species.

Globally, water supply is the major limiting factor for crop productivity. Water use efficiency (WUE) is defined as the ratio of photosynthetic carbon gain relative to water vapor loss from the leaf through the stomata to the atmosphere. Improving WUE would slow crop water use and delay the onset of drought stress when water supply does not meet crop demand. Stomatal density is an important factor that influences plant gas exchange efficiency. We have proof-of-concept that reducing stomatal density in sorghum by ubiquitously expressing a synthetic EPF2core gene can increase WUE without any decrease in photosynthetic carbon gain. However, ubiquitous expression of the synthetic EPF gene has unwanted pleiotropic effects on stem development and seed set. In this study, we test whether tissue-specific promoters can be used to isolate the desired leaf phenotypes without causing unwanted side effects. This provides an important step towards engineering stomatal density to improving WUE and protecting C4 crop yields from drought-induced losses today and in a future, warmer climate.

The maize disease lesion mimic mutants spontaneously form lesions on leaf blades and sheaths that strongly resemble the plant's responses to pathogen infection. Variations in lesion morphology, spatiotemporal distribution, and sensitivity to genetic background and weather make them ideal candidates to develop high throughput and high resolution phenotyping methods for individual plants and their organs in unstructured fields. We present three approaches to imaging lesions at different phenotyping scales and image resolution. Each strategy has distinct advantages and poses unique collection and computational challenges. The first is imaging individual leaves ex situ before sexual maturity using reflected light. The challenge is to identify leaves while the lesions are sufficiently separated for easier segmentation, yet numerous enough for good sample size and mature enough to display the range of lesion developmental stages. This is a moderate throughput, moderate resolution strategy. The second is to image plants using UAVs in situ. The challenges are to fly low enough for good lesion resolution while minimizing extraneous movement and to register individual plants and their leaves during the growing season. This is a high throughput, lower to moderate resolution strategy. The third is to image lesions using after-market lenses on cell phones in situ. The challenges are to capture the same region of the leaves over time without interfering with lesion formation and to mosaic the imagery of highly repetitive surface features into a summary view for registration. This is a low throughput, high resolution strategy.

Background Cardiovascular complications are frequently reported among patients with pulmonary coronavirus disease 2019 (COVID-19) infection. Echocardiography has been immensely implemented for diagnosing cardiovascular involvements. We aimed to evaluate the changes in echocardiographic parameters in health care workers infected with COVID-19 during follow-up. Methods This prospective study was conducted during Iran’s third COVID-19 wave in November 2020 among health care workers who were infected with COVID-19 but otherwise healthy. A total of 100 patients underwent echocardiographic examination six to eight weeks following recovery, an early follow-up. Six months after the COVID-19 diagnosis, as the late follow-up, 63 subjects underwent echocardiographic evaluations. Moreover, based on clinical and radiological evidence, individuals were categorized into two groups of non-severe and severe COVID-19. Results The participants’ mean age was 40.4±8.1 years. In the non-severe COVID-19 group, Right Ventricle Free-Wall Global Longitudinal Strain (RVFWGLS) significantly decreased in the follow-up echocardiogram (-32.3±4.6% vs. -28.8±5.8%, p-value=0.002). RV Fraction Area Change (RV-FAC) (46.6% [43.6-53] vs. 39.7% [25-43] , p-value <0.001) and, Tricuspid Annular Plane Systolic Excursion (TAPSE) (21 mm [19-24] vs. 23 mm [20-25], p-value=0.09) did not show a significant change. In the severe COVID-19 group in late echocardiogram, RVFWGLS showed no statistically significant change (-28.3%±3.5 vs. -28.6%±5.1, p-value=0.79). The RV-FAC (47.2% [42.3-52.2] vs. 36.4% [31.1-45], p-value=0.002) showed a significant decrease, and TAPSE (22.5 mm [19.1-24.2] vs. 23 mm [21-25], p-value= 0.55) was comparable. Conclusion Although LV and RV functions did not vary significantly over time in our entire cohort, different patterns of changes were discovered according to baseline function.

The conflict in Ukraine and ensuing energetic crisis has hindered environmental policies in Europe and elsewhere. The war itself has severe environmental implications. The conflict between two major agricultural countries has negative impact on the global food supply. As food prices rise, some nations are likely to cope by converting forests and grasslands to fields. International tensions and conflicts are among reasons to boost childbearing in Russia and some other countries. There are inter-ethnic differences in the birthrate within Russia and worldwide. The necessity of birth control has been obfuscated by conflicting national and global interests, the population growth being regarded as a tool helping to the national sovereignty and defense. The pro-natalist policy is counterproductive in view of the global overpopulation. The ecological damage is generally proportional to the population density. The demographic growth contributes to the scarcity of energy and food in many regions. The energy could be supplied by nuclear power plants (NPPs). Well-managed NPPs pose less of a risk than those running on fossil fuels. The nuclear facilities practically do not emit greenhouse gases. Obviously, a lasting peace is needed, since NPPs are potential targets. By analogy with the Chernobyl accident, the war damage and shutdown of the Zaporozhie NPP (the largest NPP in Europe) will enhance demands for fossil fuels. In the past, the overpopulation was counteracted by wars, pestilence and famine. Today, scientifically based humane methods can be used to regulate the population size taking account of ecological and economical conditions in different regions. Large projects could be accomplished to improve the quality of life all over the world: irrigation systems, nuclear and other energy sources instead of fossil fuels. Hydroelectric power plants can be built on large rivers to produce hydrogen as eco-friendly energy carrier. Such projects would create many jobs, being a reasonable alternative to hostilities and excessive military expenditures. Not only durable peace but also mutual trust is required for that. Ukraine should become a testing ground for the international trust and cooperation.

We investigate the robustness of 3D point-based deep learning for organ segmentation of 3D plant models against varying reconstruction quality of the surface. The reconstruction quality is quantified in two ways: 1) The number of acquisitions for partial 3D scans and 2) the amount of noise. High quality models of real rosebush plants are used to collect point clouds in a controlled simulation environment as a way to degrade surface quality systematically. We show that the well-known 3D point-based neural network PointNet++ is capable of operating effectively on low quality and corrupted data for the task of plant organ segmentation. The results indicate that investing on developing deep learning methods has the potential of advancing applications of automated phenotyping, especially for low-quality 3D point clouds of plants. Keywords: plant phenotyping, organ segmentation, robustness analysis, point-based deep learning (a) (b) Figure 1: A 3D rosebush model from ROSE-X data set: (a) point cloud; (b) triangular mesh model.

Climate change and harsh agricultural practices are increasing the amount of salt and heavy metals in soil, drastically decreasing the amount of arable land while simultaneously lowering crop yields. However, some plants grown in poor soil have adapted diverse mechanisms to cope with harsh environments. It has been hypothesized that the biochemical mechanisms responsible for salt tolerance overlaps with heavy metal tolerance, yet the similarities in these mechanisms are still unknown. Lessons from naturally salt and heavy metal tolerant plants can be applied to crops to increase resilience or be used in phytoremediation efforts. Here, we use the salt and heavy metal tolerant plant Cakile maritima as a model system for phytoremediation by using a large-scale multi-omics approach, combining ionomics, metabolomics, transcriptomics, and genomics, to understand the metabolic responses following NaCl and cadmium stress. We have developed an automated pipeline for tracking salinity, as well as using elemental analysis to monitor intracellular concentrations. We will perform RNA-seq to understand patterns of differential gene expression, gather a list of candidate genes, and use comparative genomics to understand the potential influence of ancient polyploidy on stress tolerance. Combining this with metabolomics will enable a fully integrated understanding of salt stress response and allow us to know if Cakile maritima is predisposed for salt stress or has a rapid stress response. Coupling this with transcriptomics will allow us to identify important pathways and neofunctionalized genes that may be specific for C. maritima stress response and be applied to crop species to enhance resilience.

Background and Purpose: Diabetic nephropathy (DN) closely relates to morphological and functional changes in podocytes, and anaerobic glycolysis represents the predominant energy source of podocytes. However, it is unknown whether lactate accumulation in chronic high glucose caused epithelial-mesenchymal transition (EMT) of podocytes through lactate-derived histone lactylation. Experimental Approach: We examined biomarkers of podocytes and mesenchymal cells as well as lactylation of histone lysine residues (HKla) in mouse MPC cells cultured with high glucose (HG) or lactate (LA). Moreover, these indices were observed in MPCs after HG co-culture with multiple interventions of lactate levels, and differently expressed genes (DEGs) were screened using RNA sequencing. Finally, renal pathological characteristics and histone lactylation were investigated in diabetic mice with lactate-lowering treatments. Key Results: Both HG and LA decreased nephrin levels while increased collagen IV levels in MPCs, and HG and LA stimulation synchronously elevated HKla levels. However, co-treatment with oxamate or dichloroacetate reducing lactate levels alleviated decreases in nephrin and ZO-1 levels and increases in collagen IV and α smooth muscle actin levels as well as HKla levels in HG-cultured MPCs, but co-treatment with rotenone diversely affected these indices. RNA sequencing found eleven DEGs in HG-cultured MPCs after oxamate or dichloroacetate intervention and qPCR experiments validated four of them. Importantly, oxamate or dichloroacetate treatment attenuated renal functions, EMT, and histone lactylation in kidney of diabetic mice. Conclusion and Implications: This study clarified that lactate mediated chronic high glucose-caused podocyte EMT through lactate-induced histone lactylation, and then promoted the pathological process of DN.

Field Based High Throughput Phenotyping Enables the Discovery of Loci Linked to Senescence and Grain Filling Period ORCiD: [Alper Adak; 0000-0002-2737-8041] Keywords: Grain filling period, field-based high throughput phenotyping, days to senescence, temporal phenotype. Senescence occurs progressively over time and is variable among different genotypes. To examine the temporal and genetic variation of senescence, 280 maize hybrids and 520 maize recombinant inbred lines (RILs) grown in 2017 and 2018 were investigated. Hybrids were grown in late and optimal planting trials; RILs were grown in irrigated and non-irrigated trials, both based on range-row design with two replications. Two types of Unmanned aerial systems (UAS, also known as UAV or drones) were flown over the germplasm between 14 and 20 times respectively. Temporal senescence of each row-plot in hybrids and RILs was scored visually according to percentile scoring using four to five rectified drone images between ~90 and ~130 days after planting. A mechanistic growth model was fit to each genotype using the temporal senescence scores, resulting in 0.94 and 0.97 R 2 for hybrids and RILs. Days to senescence (DTSE) and grain filling period (GFP) were calculated for each row plot using the developed mechanistic growth model. To predict the genotypic value for each RIL and hybrid, a mixed model with three-way interaction model (Genotype*Flight*Environment) was then run. Correlation was calculated ~0.84 and ~0.88 between grain yield and GFP and DTSE in hybrids. A major quantitative trait locus was also discovered on chromosome 1 (295.5 to 296.8 kb; 15% explained) linked to GFP in RILs. GFP is known to be physiologically important, UAS provided an easily scalable measure which can greatly increase the evaluation of variation in the field.

ORCiD: [0000-0002-7283-3357] Plant Scientists are striving to improve crop response to abiotic stress under adverse environmental conditions. Many bio-physical , biochemical, and physiological traits are difficult to quantify due to the low throughput and destructive nature for their measurements. This study aims to characterize biophysical and physiological traits of maize plants using RGB and hyperspectral imaging in greenhouse condition. Single hybrid Maize genotype with four different treatment combination of water and nitrogen were tested. Plants were imaged, harvested, and measured at several growth stages range from V6 to R5 stages. Images were analyzed and correlation was established between manually measured plant traits and pixel level information extracted from the plants. RGB images are processed to determine projected plant area which are correlated with destructively measured plant shoot fresh weight, dry weight, and biomass area. Hyperspectral images are processed to extract plant leaf reflectance and correlated with leaf nitrogen/chlorophyll content. PLSR models are calibrated to estimate corn leaf nitrogen/chlorophyll content from image-generated hyperspectral data, as well as the leaf hyperspectral data from a handheld ASD spectrometer and their performance will be compared. Biological science, computer vision, mathematics and engineering can be integrated as a holistic approach for quantifying the overall growth, development, and response of maize plants under differential nitrogen rates.