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2. PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Author

Xu, Weihuang, Yu, Guohao, Cui, Yiming, Gloaguen, Romain, Zare, Alina, Bonnette, Jason, Reyes-Cabrera, Joel, Rajurkar, Ashish, Rowland, Diane, Matamala, Roser, Jastrow, Julie D., Juenger, Thomas E., and Fritschi, Felix B.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms., Comment: The 36th AAAI Conference on the AI for Agriculture and Food Systems (AIAFS) Workshop

Published
2022

7. Peanut Maturity Classification using Hyperspectral Imagery

Author

Zou, Sheng, Tseng, Yu-Chien, Zare, Alina, Rowland, Diane, Tillman, Barry, and Yoon, Seung-Chul

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Seed maturity in peanut (Arachis hypogaea L.) determines economic return to a producer because of its impact on seed weight (yield), and critically influences seed vigor and other quality characteristics. During seed development, the inner mesocarp layer of the pericarp (hull) transitions in color from white to black as the seed matures. The maturity assessment process involves the removal of the exocarp of the hull and visually categorizing the mesocarp color into varying color classes from immature (white, yellow, orange) to mature (brown, and black). This visual color classification is time consuming because the exocarp must be manually removed. In addition, the visual classification process involves human assessment of colors, which leads to large variability of color classification from observer to observer. A more objective, digital imaging approach to peanut maturity is needed, optimally without the requirement of removal of the hull's exocarp. This study examined the use of a hyperspectral imaging (HSI) process to determine pod maturity with intact pericarps. The HSI method leveraged spectral differences between mature and immature pods within a classification algorithm to identify the mature and immature pods. The results showed a high classification accuracy with consistency using samples from different years and cultivars. In addition, the proposed method was capable of estimating a continuous-valued, pixel-level maturity value for individual peanut pods, allowing for a valuable tool that can be utilized in seed quality research. This new method solves issues of labor intensity and subjective error that all current methods of peanut maturity determination have.

Published
2019

8. Overcoming Small Minirhizotron Datasets Using Transfer Learning

Author

Xu, Weihuang, Yu, Guohao, Zare, Alina, Zurweller, Brendan, Rowland, Diane, Reyes-Cabrera, Joel, Fritschi, Felix B, Matamala, Roser, and Juenger, Thomas E.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Minirhizotron technology is widely used for studying the development of roots. Such systems collect visible-wavelength color imagery of plant roots in-situ by scanning an imaging system within a clear tube driven into the soil. Automated analysis of root systems could facilitate new scientific discoveries that would be critical to address the world's pressing food, resource, and climate issues. A key component of automated analysis of plant roots from imagery is the automated pixel-level segmentation of roots from their surrounding soil. Supervised learning techniques appear to be an appropriate tool for the challenge due to varying local soil and root conditions, however, lack of enough annotated training data is a major limitation due to the error-prone and time-consuming manually labeling process. In this paper, we investigate the use of deep neural networks based on the U-net architecture for automated, precise pixel-wise root segmentation in minirhizotron imagery. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. We trained three neural networks with different architectures on the larger peanut root dataset to explore the effect of the neural network depth on segmentation performance. To tackle the more limited switchgrass root dataset, we showed that models initialized with features pre-trained on the peanut dataset and then fine-tuned on the switchgrass dataset can improve segmentation performance significantly. We obtained 99\% segmentation accuracy in switchgrass imagery using only 21 training images. We also observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset are more effective than features pre-trained on the large but unrelated ImageNet dataset.

Published
2019
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9. Cross‐Generational Effect of Water Deficit Priming on Physiology of Peanut Plants Under Water Stress.

Author

de Camargo Santos, Aline, Schaffer, Bruce, Rowland, Diane, Bremgartner, Matthew, Moon, Pamela, Tillman, Barry, Rodrigues de Souza, Edivan, and Bassil, Elias

Subjects
PLANT physiology, PLANT-water relationships, WATER efficiency, AQUATIC plants, DEFICIT irrigation, WATER supply
Abstract

Water deficit priming through regulated deficit irrigation has been shown to be beneficial for peanut cultivation, leading to improved water‐use efficiency during the crop cycle and enhanced stress acclimation. The effects of priming using water deficit can be heritable, but little is known about stress priming effects on the physiology and growth of successive generations undergoing water stress. Two experiments were conducted to assess cross‐generational priming by determining physiological and growth responses of offspring of primed and non‐primed peanut plants of two genotypes, COC‐041 and New Mexico Valencia C (NMV‐C), both previously found to be strongly responsive to priming. Seeds were collected from parental plants subjected to mild water stress by regulated deficit irrigation (primed) or adequate irrigation (non‐primed). These seeds were then planted, and the offspring were monitored for physiological and growth responses to water stress, including on a whole‐plant basis using a high‐throughput physiological phenotyping platform and on individual leaves by periodic single‐leaf measurements. Measurements included whole‐plant transpiration (plant‐Tr), root water uptake, leaf transpiration, stomatal conductance and net CO2 assimilation (leaf‐Tr, leaf‐gs, and leaf‐A), leaf water and osmotic potential (leaf‐Ψw and leaf‐Ψo), leaf osmotic adjustment, leaf relative water content (leaf‐RWC) and cumulative plant‐Tr. Offspring of both genotypes from primed parent plants had faster early establishment, with more uniform germination, and more rapid initial seedling growth compared to offspring from non‐primed parent plants. Although offspring of both non‐primed and primed plants of both genotypes exhibited a significant reduction of plant‐Tr, gas exchange, leaf‐Ψw, leaf‐Ψo, and leaf‐RWC when exposed to water stress, offspring of primed plants showed increased water use efficiency through reduced leaf‐gs, leaf‐Tr and plant‐Tr while maintaining leaf‐A under water stress. Despite offspring of both primed and non‐primed plants being susceptible to severe water stress, offspring of primed plants exhibited overall enhanced water use efficiency, leading to greater dry biomass production per gram of transpired water and a trend of less growth reduction due to water stress compared to offspring of non‐primed plants, especially for the genotype COC‐041. This study shows the potential of water deficit priming to promote cross‐generational changes in physiological function under limited water availability, by enhancing crop stress acclimation in the next plant generation. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Age-related reduction in motor adaptation: brain structural correlates and the role of explicit memory

Author

Tyler, Lorraine K., Brayne, Carol, Bullmore, Edward T., Calder, Andrew C., Cusack, Rhodri, Dalgleish, Tim, Duncan, John, Matthews, Fiona E., Marslen-Wilson, William D., Shafto, Meredith A., Campbell, Karen, Cheung, Teresa, Davis, Simon, Geerligs, Linda, Kievit, Rogier, McCarrey, Anna, Mustafa, Abdur, Price, Darren, Samu, David, Taylor, Jason R., Treder, Matthias, van Belle, Janna, Williams, Nitin, Bates, Lauren, Emery, Tina, Erzinçlioglu, Sharon, Gadie, Andrew, Gerbase, Sofia, Georgieva, Stanimira, Hanley, Claire, Parkin, Beth, Troy, David, Auer, Tibor, Correia, Marta, Gao, Lu, Green, Emma, Henriques, Rafael, Allen, Jodie, Amery, Gillian, Amunts, Liana, Barcroft, Anne, Castle, Amanda, Dias, Cheryl, Dowrick, Jonathan, Fair, Melissa, Fisher, Hayley, Goulding, Anna, Grewal, Adarsh, Hale, Geoff, Hilton, Andrew, Johnson, Frances, Johnston, Patricia, Kavanagh-Williamson, Thea, Kwasniewska, Magdalena, McMinn, Alison, Norman, Kim, Penrose, Jessica, Roby, Fiona, Rowland, Diane, Sargeant, John, Squire, Maggie, Stevens, Beth, Stoddart, Aldabra, Stone, Cheryl, Thompson, Tracy, Yazlik, Ozlem, Barnes, Dan, Dixon, Marie, Hillman, Jaya, Mitchell, Joanne, Villis, Laura, Wolpe, Noham, Ingram, James N., Tsvetanov, Kamen A., Henson, Richard N., Wolpert, Daniel M., and Rowe, James B.

Published
2020
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17. Strong and specific associations between cardiovascular risk factors and white matter micro- and macrostructure in healthy aging

Author

Tyler, Lorraine K., Brayne, Carol, Bullmore, Edward T., Calder, Andrew C., Cusack, Rhodri, Dalgleish, Tim, Duncan, John, Henson, Richard N., Matthews, Fiona E., Marslen-Wilson, William D., Rowe, James B., Shafto, Meredith A., Campbell, Karen, Cheung, Teresa, Davis, Simon, Geerligs, Linda, Kievit, Rogier, McCarrey, Anna, Mustafa, Abdur, Price, Darren, Samu, David, Taylor, Jason R., Treder, Matthias, Tsvetanov, Kamen, van Belle, Janna, Williams, Nitin, Bates, Lauren, Emery, Tina, Erzinlioglu, Sharon, Gadie, Andrew, Gerbase, Sofia, Georgieva, Stanimira, Hanley, Claire, Parkin, Beth, Troy, David, Auer, Tibor, Correia, Marta, Gao, Lu, Green, Emma, Henriques, Rafael, Allen, Jodie, Amery, Gillian, Amunts, Liana, Barcroft, Anne, Castle, Amanda, Dias, Cheryl, Dowrick, Jonathan, Fair, Melissa, Fisher, Hayley, Goulding, Anna, Grewal, Adarsh, Hale, Geoff, Hilton, Andrew, Johnson, Frances, Johnston, Patricia, Kavanagh-Williamson, Thea, Kwasniewska, Magdalena, McMinn, Alison, Norman, Kim, Penrose, Jessica, Roby, Fiona, Rowland, Diane, Sargeant, John, Squire, Maggie, Stevens, Beth, Stoddart, Aldabra, Stone, Cheryl, Thompson, Tracy, Yazlik, Ozlem, Barnes, Dan, Dixon, Marie, Hillman, Jaya, Mitchell, Joanne, Villis, Laura, Fuhrmann, Delia, Nesbitt, David, Shafto, Meredith, and Kievit, Rogier A.

Published
2019
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28. Idiosyncratic responding during movie-watching predicted by age differences in attentional control

Author

Tyler, Lorraine K., Brayne, Carol, Bullmore, Ed, Calder, Andrew, Cusack, Rhodri, Dalgleish, Tim, Duncan, John, Henson, Rik, Matthews, Fiona, Marslen-Wilson, William, Rowe, James, Shafto, Meredith, Campbell, Karen, Cheung, Teresa, Davis, Simon, Geerligs, Linda, Kievit, Rogier, McCarrey, Anna, Price, Darren, Taylor, Jason, Tsvetanov, Kamen, Williams, Nitin, Bates, Lauren, Emery, Tina, Erzinçlioglu, Sharon, Gadie, Andrew, Gerbase, Sofia, Georgieva, Stanimira, Hanley, Claire, Parkin, Beth, Troy, David, Allen, Jodie, Amery, Gillian, Amunts, Liana, Barcroft, Anne, Castle, Amanda, Dias, Cheryl, Dowrick, Jonathan, Fair, Melissa, Fisher, Hayley, Goulding, Anna, Grewal, Adarsh, Hale, Geoff, Hilton, Andrew, Johnson, Frances, Johnston, Patricia, Kavanagh-Williamson, Thea, Kwasniewska, Magdalena, McMinn, Alison, Norman, Kim, Penrose, Jessica, Roby, Fiona, Rowland, Diane, Sargeant, John, Squire, Maggie, Stevens, Beth, Stoddart, Aldabra, Stone, Cheryl, Thompson, Tracy, Yazlik, Ozlem, Dixon, Marie, Barnes, Dan, Hillman, Jaya, Mitchell, Joanne, Villis, Laura, Campbell, Karen L., Shafto, Meredith A., Wright, Paul, and Tsvetanov, Kamen A.

Published
2015
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31. NAPPN Annual Conference Abstract: A Pipeline for Individual Root Feature Extraction in Minirhizotron Image

Author

Cui, Yiming, primary, Xu, Weihuang, additional, Yu, Guohao, additional, Gloaguen, Romain, additional, Zare, Alina, additional, Bonnette, Jason, additional, Reyes-Cabrera, Joel, additional, Rajurkar, Ashish B, additional, Rowland, Diane, additional, Jastrow, Julie D, additional, Juenger, Thomas E, additional, and Fritschi, Felix B, additional

Published
2022
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32. NAPPN Annual Conference Abstract: Weakly-supervised Plant Root Segmentation with Graph Convolutional Networks

Author

Cui, Yiming, primary, Xu, Weihuang, additional, Yu, Guohao, additional, Gloaguen, Romain, additional, Zare, Alina, additional, Bonnette, Jason, additional, Reyes-Cabrera, Joel, additional, Rajurkar, Ashish B, additional, Rowland, Diane, additional, Jastrow, Julie D, additional, Juenger, Thomas E, additional, and Fritschi, Felix B, additional

Published
2022
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33. Distinct components of cardiovascular health are linked with age-related differences in cognitive abilities

Author

King, Deborah LO, Henson, Richard N, Kievit, Rogier, Wolpe, Noham, Brayne, Carol, Tyler, Lorraine K, Rowe, James B, Bullmore, Edward T, Calder, Andrew C, Cusack, Rhodri, Dalgleish, Tim, Duncan, John, Matthews, Fiona E, Marslen-Wilson, William D, Shafto, Meredith A, Campbell, Karen, Cheung, Teresa, Davis, Simon, Geerligs, Linda, McCarrey, Anna, Mustafa, Abdur, Price, Darren, Samu, David, Taylor, Jason R, Treder, Matthias, van Belle, Janna, Williams, Nitin, Mitchell, Daniel, Fisher, Simon, Eising, Else, Knights, Ethan, Bates, Lauren, Emery, Tina, Erzinçlioglu, Sharon, Gadie, Andrew, Gerbase, Sofia, Georgieva, Stanimira, Hanley, Claire, Parkin, Beth, Troy, David, Auer, Tibor, Correia, Marta, Gao, Lu, Green, Emma, Henriques, Rafael, Allen, Jodie, Amery, Gillian, Amuntse, Liana, Barcroft, Anne, Castle, Amanda, Dias, Cheryl, Dowrick, Jonathan, Fair, Melissa, Fisher, Hayley, Goulding, Anna, Grewale, Adarsh, Hale, Geoff, Hilton, Andrew, Johnson, Frances, Johnston, Patricia, Kavanagh-Williamson, Thea, Kwasniewska, Magdalena, McMinn, Alison, Norman, Kim, Penrose, Jessica, Roby, Fiona, Rowland, Diane, Sargeant, John, Squire, Maggie, Stevens, Beth, Stoddart, Aldabra, Stone, Cheryl, Thompson, Tracy, Yazlik, Ozlem, Barnes, Dan, Dixon, Marie, Hillman, Jaya, Mitchell, Joanne, Villis, Laura, Tsvetanov, Kamen A, and Apollo - University of Cambridge Repository

Subjects
692/4019/592, 631/378/2612, article, 692/499
Abstract

Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18–88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.

Published
2023
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40. Medicaid's role in health reform and closing the coverage gap

Author

Rowland, Diane and Lyons, Barbara

Subjects
Medicaid -- Laws, regulations and rules, Government regulation, Health, Law, Patient Protection and Affordable Care Act
Abstract

Medicaid coverage matters for millions of low-income Americans, and especially for those with ongoing and serious health challenges. A source of comprehensive and affordable coverage, Medicaid has long been a [...]

Published
2016
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