Ballenger, J. G. | bioRxiv Science

bioRxiv2025

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗

bioRxiv2024

Reduced leaf numbers due to shade avoidance allows sugar beet to maintain biomass under drought stress

The physical environment is complex even in managed agroecosystems, and plants often experience multiple stressors such as light competition and drought simultaneously. Phenotypic responses to multiple stressors may exhibit tradeoffs or constraints, such that harvestable yield is reduced In this study, we investigate how sugar beets respond to combinations of neighbor presence, resource competition, and water stress, and test the hypothesis that shade avoidance cues from neighboring vegetation may cause yield loss similar to, or even greater than, resource depletion by neighboring vegetation. Sugar beets were grown in 19L pails surrounded by Kentucky bluegrass (Poa pratiensis). Competition treatments included "shade avoidance only" where B. vulgaris leaves were exposed to reflected light from neighbors without shading or root competition, "shade avoidance plus root interaction" where crop and competitor roots interacted, and "no competition" as a control. Sugar beets in each competition treatment were subjected to 3 levels of irrigation: 100% of estimated evapotranspiration (ET), 80% ET and 60% ET. When grown without drought stress (100% ET), the shade avoidance treatment had 16% less root biomass compared to the no competition control (P = 0.007); however, under the most severe drought treatment (60% ET) root biomass was similar between the shade avoidance and no competition treatments (P = 0.48). Under 60% ET, plants in the shade avoidance only treatment produced 15% fewer leaves (P < 0.001) but 14% greater leaf area (P = 0.049) compared to the no competition control. The reduced number of leaves produced due to shade avoidance resulted in less structural tissue to be maintained under drought stress, and thus provides a potential benefit to shade avoidance responses that are not directly related to avoiding shade.

plant biology↗