The study examined leaf pavement cell shape complexity across a natural European aspen (Populus tremula) population, using GWAS to pinpoint the transcription factor MYB305a as a regulator of cell geometry. Functional validation showed that MYB305a expression is induced by drought and contributes to shape simplification, with cell complexity negatively correlated with water-use efficiency and climatic variables of the genotypes' origin.
The authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.
PlantCV v4: Image analysis software for high-throughput plant phenotyping
Authors: Schuhl, H., Brown, K. E., Sheng, H., Bhatt, P. K., Gutierrez, J., Schneider, D., Casto, A. L., Acosta-Gamboa, L., Ballenger, J. G., Barbero, F., Braley, J., Brown, A. M., Chavez, L., Cunningham, S., Dilhara, M., Dimech, A. M., Duenwald, J. G., Fischer, A., Gordon, J. M., Hendrikse, C., Hernandez, G. L., Hodge, J. G., Huber, M., Hurr, B. M., Jarolmasjed, S., Medina Jimenez, K., Kenney, S., Konkel, G., Kutschera, A., Lama, S., Lohbihler, M., Lorence, A., Luebbert, C., Ly, N., Manching, H. K., Marrano, A., Meerdink, S., Miklave, N. M., Mudrageda, P., Murphy, K. M., Peery, J. D., Pierik, R., Polyd
PlantCV v4 is an open-source Python framework that simplifies image-based plant phenotyping by providing extensive tutorials and streamlined installation, enabling users with limited coding skills to automate trait extraction. The release adds support for fluorescence, thermal, and hyperspectral imaging and introduces a new subpackage for morphological measurements such as leaf angle, which is validated against manual data collection methods.
The study combined high-throughput image-based phenotyping with genome-wide association studies to uncover the genetic architecture of tolerance to the spittlebug Aeneolamia varia in 339 interspecific Urochloa hybrids. Six robust QTL were identified for plant damage traits, explaining up to 21.5% of variance, and candidate genes linked to hormone signaling, oxidative stress, and cell‑wall modification were highlighted, providing markers for breeding.
Phenotypic scoring of Canola Blackleg severity using machine learning image analysis
Authors: Hu, Q., Anderson, S. N., Gardner, S., Ernst, T. W., Koscielny, C. B., Bahia, N. S., Johnson, C. G., Jarvis, A. C., Hynek, J., Coles, N., Falak, I., Charne, D. R., Ruidiaz, M. E., Linares, J. N., Mazis, A., Stanton, D. J.
The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.