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Unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer high spatial and temporal resolution imagery for monitoring crop stress at early stages of development. Analysis of UAV-derived data with advanced machine learning models could improve real-time management in agricultural systems, but guidance for this integration is currently limited. Here we compare two deep learning-based strategies for early warning detection of crop stress, using multitemporal imagery throughout the growing season to predict field-scale yield in irrigated rice in eastern Arkansas. Both deep learning strategies showed improvements upon traditional statistical learning approaches including linear regression and gradient boosted decision trees. First, we explicitly accounted for variation across developmental stages using a 3D convolutional neural network (CNN) architecture that captures both spatial and temporal dimensions of UAV images from multiple time points throughout one growing season. 3D-CNNs achieved low prediction error on the test set, with a Root Mean Squared Error (RMSE) of 8.8% of the mean yield. For the second strategy, a 2D-CNN, we considered only spatial relationships among pixels for image features acquired during a single flyover. 2D-CNNs trained on images from a single day were most accurate when images were taken during booting stage or later, with RMSE ranging from 7.4 to 8.2% of the mean yield. A primary benefit of convolutional autoencoder-like models (based on analyses of prediction maps and feature importance) is the spatial denoising effect that corrects yield predictions for individual pixels based on the values of vegetation index and thermal features for nearby pixels. Our results highlight the promise of convolutional autoencoders for UAV-based yield prediction in rice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Bellis, Hashem, Causey, Runkle, Moreno-García, Burns, Green, Burcham, Reba and Huang.)

The communication between chloroplasts and mitochondria, which depends on the inter-organellar exchange of carbon skeletons, energy, and reducing equivalents, is essential for maintaining efficient respiratory metabolism and photosynthesis. We devised a multi-transgene approach to manipulate the leaf energy and redox balance in tobacco (Nicotiana tabacum) while monitoring the in vivo cytosolic redox status of NAD(H) using the biosensor c-Peredox-mCherry. Our strategy involved altering the shuttling capacity of the chloroplast by (1) increasing the chloroplast malate valve capacity by overexpression of the chloroplast malate valve transporter pOMT from Arabidopsis (AtpOMT1) while (2) reducing the activity of the chloroplast triose-phosphate/3-phosphoglycerate shuttle by knocking down the cytosolic NAD-dependent glyceraldehyde 3-phosphate dehydrogenase (NtGAPC). This was accompanied by (3) alterations to the export of reducing equivalents in the mitochondria by knocking down the mitochondrial malate dehydrogenase (NtmMDH) and (4) an increased expression of the mitochondrial fission regulator FIS1A from Arabidopsis (AtFIS1A). The multi-transgene tobacco plants were analysed in glasshouse conditions and showed significant increases in the cytosolic NADH:NAD + in the dark when transcript levels for NtGAPC or NtmMDH were knocked down. In addition, principal component analysis and Spearman correlation analyses showed negative correlations between average transcript levels for the gene targets and parameters related to chlorophyll fluorescence and plant growth. Our results highlight the importance of the shuttling of energy and reducing equivalents from chloroplasts and mitochondria to support photosynthesis and growth and suggest an important role for the dual 2-oxoglutarate/malate and oxaloacetate/malate transporter (pOMT)., (Copyright © 2021 Elsevier GmbH. All rights reserved.)

Irrigated rice requires intense water management under typical agronomic practices. Cost effective tools to improve the efficiency and assessment of water use is a key need for industry and resource managers to scale ecosystem services. In this research we advance model-based decomposition and machine learning to map inundated rice using time-series polarimetric, L -band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) observations. Simultaneous ground truth observations recorded water depth inundation during the 2019 crop season using instrumented fields across the study site in Arkansas, USA. A three-component model-based decomposition generated metrics representing surface-, double bounce-, and volume-scattering along with a shape factor, randomness factor, and the Radar Vegetation Index (RVI). These physically meaningful metrics characterized crop inundation status independent of growth stage including under dense canopy cover. Machine learning (ML) comparisons employed Random Forest (RF) using the UAVSAR derived parameters to identify cropland inundation status across the region. Outcomes show that RVI, proportion of the double-bounce within total scattering, and the relative comparison between the double-bounce and the volume scattering have moderate to strong mechanistic ability to identify rice inundation status with Overall Accuracy (OA) achieving 75%. The use of relative ratios further helped mitigate the impacts of far range incidence angles. The RF approach, which requires training data, achieved a higher OA and Kappa of 88% and 71%, respectively, when leveraging multiple SAR parameters. Thus, the combination of physical characterization and ML provides a powerful approach to retrieving cropland inundation under the canopy. The growth of polarimetric L -band availability should enhance cropland inundation metrics beyond open water that are required for tracking water quantity at field scale over large areas., (© 2021. The Authors.)

Episcleral hemangiomas are usually associated with neonatal hemangiomatosis. Recently, propranolol has been described for the treatment of this entity. We present for the first time a patient with an episcleral hemangioma without neonatal hemangiomatosis successfully treated with topical timolol., (© 2014 Wiley Periodicals, Inc.)