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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. 

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