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Understanding three‐dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field‐grown roots remains a challenge due to the unknown tradeoff between 3D root‐model quality and 3D root‐trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state‐of‐the‐art open‐source 3D model reconstruction pipelines on 12 contrasting genotypes of field‐grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch‐based Multi‐View Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi‐View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. In the second test, we compared the accuracy of 3D root‐trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP‐based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM‐based 3D reconstruction on the same dataset of 12 genotypes, with 5–10 replicates per genotype. The results revealed that (1) the average number of images needed to build a denser 3D model was reduced from 3000 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (DIRT/3D [COLMAP‐based 3D reconstruction]); (2) denser 3D models helped improve the accuracy of the 3D root‐trait measurement; (3) reducing the number of images can help resolve data storage problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.

 

Micronutrient deficiency or “hidden hunger” is estimated to affect two billion people worldwide and increasing the micronutrient concentration of food could play an important role in tackling this global challenge. Using a combination of imaging techniques and atomic absorption spectroscopy, we describe a link between root phenotype and micronutrient concentration in cassava, which could enable new phenotypic selection strategies for breeding. This approach could be used with existing breeding infrastructure to enhance the micronutrient concentration of cassava and hence, benefit the health of people, particularly in low‐income countries where cassava is consumed as a staple crop.

Cassava storage roots are a staple food in low‐income countries of South‐East Asia and sub‐Saharan Africa, where growth stunting is prevalent as a consequence of micronutrient deficiencies. We aim to link phenotypes of field‐grown cassava roots to micronutrient concentration in the edible storage roots as a simple way to improve phenotypic selection for nutritional value in cassava.

We used existing and newly developed imaging techniques to quantify root phenotypes of the cassava root architecture over time and used flame atomic absorption spectroscopy to measure micronutrient concentration in storage roots. Both together allow the association of root phenotypes with micronutrient concentration in mature cassava roots.

We show that early and late bulking genotypes in cassava exhibit distinct foraging behaviors that are associated with micronutrient concentration in the edible storage root. Our observations suggest that late bulking cassava is a key to provide sufficient micronutrients in the edible storage root.

The association between root phenotype and micronutrient concentration with imaging techniques allows phenotypic selection for enhanced micronutrient concentration. Therefore, implementing image‐based phenotyping into cassava breeding programs in sub‐Saharan Africa and South‐East Asia could be an essential element to resolve micronutrient deficiencies that puts individuals at a higher risk of growth stunting.

 

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 

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. 

Root-root interactions alter the architectural organization of individual root systems and therefore, affect nutrient foraging (O’Brien et al., 2005). Past reports have shown detrimental and beneficial effects to the amount of yield in crops as they avoid or prefer belowground competition (Li et al., 2006; O’Brien et al., 2005). With little research done into root-root interactions there is still much to discover about the root phenotypes arising from root-root interactions and functions. Quantifying architectural traits of root system interactions would provide insight for researchers into the benefit of a cooperation vs. competition trade-off belowground. We have begun to develop a soil filled mesocosm system to perform a series of preliminary studies using 3D imaging to develop metrics of root-root interaction using common beans (Phaseolus vulgaris). Common beans have a relatively fast growing and sparse adventitious and basal root system, making them a suitable organism for this imaging study. Our second revision of the mesocosm focused on improving and fine tuning a mesh system that provides better support for the root architecture during the soil removal process. We use a light-weight, low-visibility plastic mesh originally used as bird netting to allow image capture from all sides. Traits that we aim to extract include root growth angle, rooting depth, and root volume relative to neighbors, because these spatial qualities determine the soil areas that the root system will be foraging in. Our data will allow for the quantification and association of root plasticity in the presence of belowground competition. 

Improving root traits to improve efficiency of nutrient uptake in plants is an opportunity to increase crop production in response to climate change induced edaphic stresses. Maize (Zea mays L.) studies showed a large variation of root architecture traits in response to such stresses. Quantifying this response uses highthroughput, image-based phenotyping to characterize root architecture variation across edaphic stresses. Our objective is to test if commonly used root traits discriminate stress environments and if a single mathematical description of the complete root architecture reveals a phenotypic spectrum of root architectures in the B73 maize line using manual, DIRT/2D (Digital Imaging of Root Traits) and DIRT/3D measurements. Maize B73 inbred lines were grown in three field conditions: nonlimiting conditions, high nitrogen (N), and low N. A proprietary 3D scanner captured 2D and 3D images of harvested maize roots to compute root descriptors that distinguish shapes of root architecture. The results showed that the normalized mean value of computational root traits from DIRT/2D and DIRT/3D indicated significant discrimination among B73 across environments. We found a strong correlation (R2> 0.8) between the traits measured in 3D point clouds and manually measured traits. Ear weight and shoot biomass in low N significantly decreased by 45% and 21%, respectively. Low N reduced the maximum root system diameter by 13%, root system diameter by 10%, and root system length by 9%. The 2D and 3D whole root descriptors distinguished three different root architectural shapes of B73 in the same field. Our study assists plant breeders to improve crop productivity and stress tolerance in maize. 

Understanding root traits is essential to improve water uptake, increase nitrogen capture and accelerate carbon sequestration from the atmosphere. High-throughput phenotyping to quantify root traits for deeper field-grown roots remains a challenge, however. Recently developed open-source methods use 3D reconstruction algorithms to build 3D models of plant roots from multiple 2D images and can extract root traits and phenotypes. Most of these methods rely on automated image orientation (Structure from Motion)[1] and dense image matching (Multiple View Stereo) algorithms to produce a 3D point cloud or mesh model from 2D images. Until now the performance of these methods when applied to field-grown roots has not been compared tested commonly used open-source pipelines on a test panel of twelve contrasting maize genotypes grown in real field conditions[2-6]. We compare the 3D point clouds produced in terms of number of points, computation time and model surface density. This comparison study provides insight into the performance of different open-source pipelines for maize root phenotyping and illuminates trade-offs between 3D model quality and performance cost for future high-throughput 3D root phenotyping. DOI recognition was not working: https://doi.org/10.1002/essoar.10508794.2