@article{seethepalli_ottley_childs_cope_fine_lagergren_kalluri_iversen_york_2024, title={Divide and conquer: using R<sc>hizo</sc>V<sc>ision</sc> E<sc>xplorer</sc> to aggregate data from multiple root scans using image concatenation and statistical methods}, volume={10}, ISSN={["1469-8137"]}, DOI={10.1111/nph.20151}, abstractNote={Summary Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with R hizo V ision E xplorer , a free and open‐source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.}, journal={NEW PHYTOLOGIST}, author={Seethepalli, Anand and Ottley, Chanae and Childs, Joanne and Cope, Kevin R. and Fine, Aubrey K. and Lagergren, John H. and Kalluri, Udaya and Iversen, Colleen M. and York, Larry M.}, year={2024}, month={Oct} }
 @misc{buckner_tong_ottley_williams_2021, title={High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales}, volume={5}, ISSN={["2397-8562"]}, url={https://doi.org/10.1042/ETLS20200273}, DOI={10.1042/ETLS20200273}, abstractNote={Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, models of root architecture that provide insight into resource utilization, and the elucidation of cell-to-cell communication mechanisms that are instrumental in plant development. Image segmentation and machine learning approaches for interpreting plant image data are among many of the computational methodologies that have evolved to address challenging agricultural and biological problems. These approaches have led to contributions such as the accelerated identification of gene that modulate stress responses in plants and automated high-throughput phenotyping for early detection of plant diseases. The continued acquisition of high throughput imaging across multiple biological scales provides opportunities to further push the boundaries of our understandings quicker than ever before. In this review, we explore the current state of the art methodologies in plant image segmentation and machine learning at the agricultural, organ, and cellular scales in plants. We show how the methodologies for segmentation and classification differ due to the diversity of physical characteristics found at these different scales. We also discuss the hardware technologies most commonly used at these different scales, the types of quantitative metrics that can be extracted from these images, and how the biological mechanisms by which plants respond to abiotic/biotic stresses or genotypic modifications can be extracted from these approaches.}, number={2}, journal={EMERGING TOPICS IN LIFE SCIENCES}, publisher={Portland Press Ltd.}, author={Buckner, Eli and Tong, Haonan and Ottley, Chanae and Williams, Cranos}, editor={Jez, Joseph M. and Topp, Christopher N.Editors}, year={2021}, pages={239–248} }