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Abstract Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracy remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding. 

Abstract As with phenotyping of any microscopic appendages, such as cilia or antennae, phenotyping of root hairs has been a challenge due to their complex intersecting arrangements in two-dimensional images and the technical limitations of automated measurements. Digital Imaging of Root Traits at Microscale (DIRT/μ) is a newly developed algorithm that addresses this issue by computationally resolving intersections and extracting individual root hairs from two-dimensional microscopy images. This solution enables automatic and precise trait measurements of individual root hairs. DIRT/μ rigorously defines a set of rules to resolve intersecting root hairs and minimizes a newly designed cost function to combinatorically identify each root hair in the microscopy image. As a result, DIRT/μ accurately measures traits such as root hair length distribution and root hair density, which are impractical for manual assessment. We tested DIRT/μ on three datasets to validate its performance and showcase potential applications. By measuring root hair traits in a fraction of the time manual methods require, DIRT/μ eliminates subjective biases from manual measurements. Automating individual root hair extraction accelerates phenotyping and quantifies trait variability within and among plants, creating new possibilities to characterize root hair function and their underlying genetics. 

Challenge:  Digital Imaging of root traits 3D (DIRT/3D) [1] is a software to measure 3D root traits on excavated roots crowns from the field. However, quantifying 3D root traits remains a challenge due to the unknown tradeoff between 3D root-model quality and 3D root-trait accuracy [2].  Questions: Can the 3D root model reconstruction be improved while reducing the image-capturing effort?  Does improved 3D root model quality increase the accuracy of trait measurements? Evaluation:  Compare reconstruction performance of  five open-source 3D model reconstruction pipelines on 12 architecturally contrasting genotypes [1] of field-grown maize roots.  Evaluate the accuracy of 3D root traits between the original implementation of DIRT/3D based on VisualSFM with an implementation based on COLMAP. Conclusion:  The updated DIRT/3D (COLMAP) pipeline enables quicker image collection by reducing the number of images needed and reducing the human factor during image collection. The results demonstrate that the accuracy of 3D root-trait measurements remained uncompromised. 

Abstract Maintaining crop productivity is challenging as population growth, climate change, and increasing fertilizer costs necessitate expanding crop production to poorer lands whilst reducing inputs. Enhancing crops’ nutrient use efficiency is thus an important goal, but requires a better understanding of related traits and their genetic basis. We investigated variation in low nutrient stress tolerance in a diverse panel of cultivated sunflower genotypes grown under high and low nutrient conditions, assessing relative growth rate (RGR) as performance. We assessed variation in traits related to nitrogen utilization efficiency (NUtE), mass allocation, and leaf elemental content. Across genotypes, nutrient limitation generally reduced RGR. Moreover, there was a negative correlation between vigor (RGR in control) and decline in RGR in response to stress. Given this trade-off, we focused on nutrient stress tolerance independent of vigor. This tolerance metric correlated with the change in NUtE, plasticity for a suite of morphological traits, and leaf element content. Genome-wide associations revealed regions associated with variation and plasticity in multiple traits, including two regions with seemingly additive effects on NUtE change. Our results demonstrate potential avenues for improving sunflower nutrient stress tolerance independent of vigor, and highlight specific traits and genomic regions that could play a role in enhancing tolerance. 

Challenge : Most plant imaging systems focus predominantly on monitoring morphological traits. The challenge is to relate color information to measurements of physiological processes. Question: Can the color of individual leaves be measured and quantified over time to infer physiological information about the plant? Solution: We developed the open source and affordable plant phenotyping software pipeline for Arabidopsis thaliana. SMART (Speedy Measurement of Arabidopsis Rosette Traits) that integrates a new color analysis algorithm to measure leaf surface temperature, leaf wilting and zinc toxicity over time. Data Collection: We used public datasets to develop the algorithm [1] and validate morphological measurements. We also collected top-view images of the Arabidopsis rosette with the Open-Leaf