PlantCV is an open-source open-development image analysis software package for plant phenotyping written in Python that has been actively developed since 2014. A new version of PlantCV was recently released. Major goals of the version 4 release were to 1) simplify the process of developing workflows by reducing the amount of coding needed; 2) broadening the set of supported data types; and 3) introducing interactive annotation tools that can be used directly in PlantCV workflow notebooks. Here we highlight the use of point annotations that can be used to quickly collect sets of points for parameterization of functions such as regions of interest or the identification of landmark points. Another application of point annotations this for image annotation, which is a major bottleneck in plant phenomics. For example, we have used point annotations to analyze microscopy images aimed at measurement of quinoa salt bladders, the number and size of stomata, and scoring of pollen germination. These tasks have traditionally been low throughput and have required manual scoring, but our point annotation tools can be used along with traditional segmentation methods to semi-automatically detect and annotate images. The PlantCV point annotation tools also allow users to correct semi-automated detection results before classification (e.g., germinated vs non-germinated pollen) and extraction of size & color traits per object. Once images are annotated, results can be analyzed directly or potentially can be used as labeled data in supervised learning methods.

Imaging of plants using multi-camera arrays in high-density growth environments is a strategy for affordable high-throughput phenotyping. In multi-camera systems, simultaneous imaging of hundreds to thousands of plants eliminates the time delay in measurements between plants seen in plant-to-camera or camera-to-plant systems, which allows for the analysis of plant growth, development, and environmental responses at a high temporal resolution. On the other hand, high plant density, camera-to-camera variation, and other trade-offs increase the complexity of data analysis. Here we present two recent updates to the PlantCV image analysis package to improve usability when working with multi-plant datasets. First, we introduce a method to automate detection of plants organized in a grid layout, reducing the need to make separate workflows for each camera in a multi-camera system. Second, we reduced the number of input and output parameters for functions handling the shape and location of plants and introduce automatic iteration over multiple objects of interest (e.g. plants), reducing the level of programming needed to build workflows.