Abstract Premise Commercial plant DNA extraction kits typically include a 65°C lysis incubation step, inherited from cetyltrimethylammonium bromide (CTAB)‐based methods. Recent work suggests that cooler lysis temperatures can improve DNA purity in CTAB extractions; whether this also improves yield in CTAB‐based column kits has not been tested. Methods and Results We compared room temperature (RT, ~25°C) and 65°C lysis incubation using the Norgen Plant/Fungi DNA Isolation Kit (CTAB‐based lysis buffer) across six plant species in a paired split‐tissue design ( n = 59 pairs). RT incubation significantly increased Qubit fluorometric DNA yield by 47% overall, with a significant treatment × species interaction. RT‐extracted Cucurbita pepo DNA was compatible with genotyping‐by‐sequencing. In a focused generalization test, the modification did not improve yield with the Qiagen DNeasy Plant Mini Kit, whose lysis buffer uses the anionic detergent sodium dodecyl sulphate, indicating that the effect is specific to kit lysis chemistry. Conclusions For the Norgen kit, omitting the heating step is a zero‐cost modification that significantly improves DNA yield and the ratio of absorbance at 260 nm and 230 nm (A 260 /A 230 purity ratio) for most eudicot species tested, with no loss of downstream compatibility. This benefit appears specific to CTAB‐based lysis and should be validated for each kit and species before adoption.
Abstract Global herbarium digitization has produced over 50 million specimen images, many of which contain handwritten or printed labels with rich metadata such as species names, collection dates, localities, and ecological notes. Manual transcription of this information is labor intensive, error prone, and not scalable, which has motivated the development of automated solutions. This systematic review examines studies published between 2018 and 2026, identified through electronic database searches and backward reference screening, focusing on deep learning and computer vision approaches for herbarium label digitization. The reviewed methods include convolutional neural networks, transformer‐based models, large language models, and open‐source pipelines for optical character recognition and structured metadata extraction from historical and handwritten botanical records. The results highlight significant advances in label detection, text recognition accuracy, and metadata structuring enabled by deep learning techniques, although reported performance varies considerably depending on dataset quality, handwriting complexity, and domain adaptation strategies. Despite this progress, substantial challenges remain in handling historical handwriting styles, heterogeneous label layouts, and multilingual collections. Future research should emphasize the creation of curated benchmark datasets and the development of scalable, robust artificial intelligence (AI) pipelines to enhance the reliability, efficiency, and generalizability of automated herbarium digitization systems.
Abstract Premise Chlorophyll fluorescence (ChlF) in the light‐adapted state is a convenient indicator of plant water status but is heavily reliant on nonphotochemical quenching (NPQ). The usefulness of ChlF as an indicator can be enhanced by reducing NPQ effects. Methods ChlF and relative water content (RWC) were measured from leaves of Arabidopsis thaliana and Spinacia oleracea while the leaf samples progressively lost water. ChlF characteristics were then reformulated and tested as indicators of RWC via regression analysis. Results We demonstrate that measured NPQ varies and is by itself not a consistent indicator of RWC of plant leaves. Using NPQ as a scaler in ChlF‐based photochemical efficiency measures resulted in two NPQ‐adjusted ChlF characteristics as indicators of RWC: photosystem II photochemical quenching (PSII PQ) and an NPQ‐scaled PSII photochemical efficiency (Y(II) ′ ) measure. The results were experimentally demonstrated in A. thaliana , then validated in A. thaliana with varied illumination intensities, and finally validated in another plant ( S. oleracea ). Further analysis revealed a linear correlation between steady‐state ChlF ( F s ) and maximum fluorescence ( F m ′ ) across different RWCs and a time constant to account for the NPQ effect. Discussion This research shows that ChlF characteristics are affected by NPQ during water loss and that adjusting PSII efficiency measures based on NPQ provides enhanced effectiveness in indicating plant water status.
Abstract Premise Dry root rot (DRR), an emerging global disease exacerbated by drought, affects a wide range of crops. The disease symptoms are manually scored for large‐scale germplasm screening to identify disease‐resistant varieties and test transgenic lines and other varieties. Despite its use in the past, manual disease identification is not only cumbersome but also often incapable of distinguishing between similar stress phenotypes. Manual disease scoring is tedious, time‐consuming, and often error‐prone, especially when screening large numbers of plants. To overcome this limitation, we developed a machine learning model that employs a learning‐based approach using convolutional neural networks for automated feature extraction and image‐based identification and severity assessment of DRR. Methods Our models were trained, tested, and validated using an in‐house dataset of 10,800 unique RGB images. A total of 51 deep learning models, including single‐task and multi‐task models, were developed using the Xception and Vision Transformer (ViT) architectures with standard image augmentations, and then validated on stratified independent test sets to demonstrate robustness across camera, scanner, and microscope modalities. Results The best multi‐task ViT model, capable of scoring across combined datasets, achieved 94% accuracy in distinguishing DRR from the control and had a root mean square error of 1 in predicting DRR severity score. Discussion This model's metrics, including confusion matrix performance, underscore its potential as an efficient and accessible tool for large‐scale germplasm screening for DRR, overcoming the challenges of manual scoring.
Abstract Premise Climate change and global warming significantly affect the timing of plant flowering in spring. Accurately predicting these phenological events is crucial for understanding plant biology and agriculture. The chill‐day model (CDM) based on seasonal temperature changes has been a reliable model for predicting phenological activities; however, recent global warming has challenged its accuracy due to extreme weather variability, necessitating the adjustment of the CDM parameters to reflect recent climate change. We developed an open‐source Python package, PhenoFECT (Phenology Forecasting Exploring ClimaTe), that refines temperature‐based phenology models. The package enables efficient phenological analysis for open and user‐specified data. Methods and Results The Python package embeds open meteorological data collected over 53 years (1973–2025) in Korea for cherry ( Prunus yedoensis ), golden‐bell ( Forsythia koreana ), and azalea ( Rhododendron mucronulatum ). PhenoFECT was utilized to adjust the parameters of the CDM to reflect recent climate changes. The refined CDM improved the prediction accuracy of flowering at the average root mean square error (RMSE) of 5.74 days for three angiosperms. Conclusions PhenoFECT is a versatile tool for accurate forecasting, exploration, and visualization across diverse open meteorological data. Exploring data using PhenoFECT revealed that the CDM can be influenced by geographic features and global climate phenomena.
