Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R² of 0.62, and for LAI using MSR and NDVI an R² of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions.

Societal Impact Statement: Grapevine leaves are emblematic of the strong visual associations people make with plants. Leaf shape is immediately recognizable at a glance, and therefore, this is used to distinguish grape varieties. In an era of computationally enabled machine learning‐derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time‐honored methods, we can predict theoretical leaves that are possible, enabling us to understand the genetics, development, and environmental responses of plants in new ways.Summary: Grapevine leaves are a model morphometric system. Sampling over 10,000 leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. We measure the shapes of 139 grapevine leaves using 1672 pseudo‐landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo‐landmarks that results in a high‐resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. We visualize a high‐resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, Vitis vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. By predicting a high‐resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high‐resolution space, or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers.

Cultivar variation in seed characteristics can affect seedling vigor, and nitrogen (N) availability influences early‐season growth in some instances. However, interactions between cultivar and N application rate for early‐season growth have received limited attention. Thus, the objectives of this research were to quantify the effects of cultivar, N‐application rate, and their interaction on early‐season growth, predict seedling vigor from plant morphology, and identify associations between early‐season crop growth rates (CGR) and other growth indices. To this end, a 2‐year (2018 and 2019) field study was conducted using six cotton cultivars having different seed characteristics and six different N‐application rates. In both years, all single‐plant and whole‐crop growth indices were affected by cultivar but not early‐season N‐application rates. A function was developed that was strongly predictive (R² = 0.78) of seedling dry weight and utilized only plant height and mainstem node assessments. Leaf area index was a strong and consistent predictor of early‐season CGR, whereas net assimilation rate was only associated with CGR in one of the 2 years. Thus, it is concluded that of the two factors assessed in the current study, cultivar is the dominant driver of seedling vigor up to 35 days after planting. Additionally, leaf area development is a more consistent driver of early‐season CGR than average assimilation rates of the canopy. In contrast, an application of N at planting did not affect seedling vigor or early‐season canopy growth in cotton.

Ice encasement of annual bluegrass (Poa annua) putting greens can result in hypoxic or anoxic conditions. Organ‐specific and direct physiological evidence on the role of anaerobic metabolism and stress responses in annual bluegrass is needed to justify the importance of costly management strategies to reduce hypoxia. A two‐year controlled environment study (2022/2023–2023/2024) was conducted in East Lansing, Michigan, to investigate the impact of different durations of ice encasement stress (0, 40 and 60 days) on select reactive oxygen species (ROS), anaerobic metabolites, antioxidant enzyme activity and anaerobic enzyme activity in annual bluegrass. Ice encasement stress significantly influenced anaerobic metabolism and antioxidant responses in annual bluegrass. The content of reactive oxygen species (ROS), including superoxide anion (O₂⁻) and hydrogen peroxide (H₂O₂), increased with prolonged ice encasement, with O₂⁻ increasing 3.43‐fold and H₂O₂ increasing 3.42‐fold after 60 days of ice encasement. Antioxidant enzyme activities, such as ascorbate peroxidase (APX), catalase (CAT), peroxidase (POD) and superoxide dismutase (SOD), were upregulated in response to stress caused by prolonged ice encasement. Additionally, anaerobic respiration byproducts increased, with ethanol increasing 12.80‐fold, acetaldehyde 4.11‐fold and lactic acid 1.41‐fold after 60 days of ice encasement. Key metabolic enzymes, including pyruvate decarboxylase (PDC), alcohol dehydrogenase (ADH) and lactate dehydrogenase (LDH), also increased with ice encasement duration. Ice encasement had no significant effect on pigment content or fatty acid composition, though organ‐specific differences were observed, with roots having higher saturated fatty acids and leaves showing greater unsaturation. Therefore, we conclude that prolonged ice encasement induces anaerobic metabolism and oxidative stress in annual bluegrass, with limited antioxidant activity in crown and root tissues contributing to winterkill susceptibility. Future research should focus on determining ethanol and gas accumulation thresholds, as well as exploring management strategies to enhance antioxidant defences and mitigate hypoxic stress during overwintering.

Summary: Grapevine leaves are a model morphometric system. Sampling over ten thousand leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. We measure the shapes of 139 grapevine leaves using 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo-landmarks that results in a high-resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. We visualize a high-resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, V. vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. By predicting a high-resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis , we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high-resolution space; or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. Societal Impact Statement: Grapevine leaves are emblematic of the strong visual associations people make with plants. At a glance, leaf shape is immediately recognizable, and it is because of this reason it is used to distinguish grape varieties. In an era of computationally-enabled, machine learning-derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time-honored methods, we can predict theoretical leaves that are possible to understand the genetics, development, and environmental responses of plants in new ways.