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26 results on '"Rajurkar, Ashish"'

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. Genome-Wide Association Study Reveals Influence of Cell-specific Gene Networks on Soybean Root System Architecture

Author

Sun, Ying, primary, Miler, Charlotte, additional, Rajurkar, Ashish, additional, Lynch, Ryan, additional, Aylward, Anthony, additional, Zhang, Ling, additional, Shaner, Marieken, additional, Copeland, Charles, additional, Ye, Heng, additional, Nguyen, Henry T., additional, Busch, Wolfgang, additional, and Michael, Todd P., additional

Published
2024
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8. Fast and Efficient Root Phenotyping via Pose Estimation

Author

Berrigan, Elizabeth M., primary, Wang, Lin, additional, Carrillo, Hannah, additional, Echegoyen, Kimberly, additional, Kappes, Mikayla, additional, Torres, Jorge, additional, Ai-Perreira, Angel, additional, McCoy, Erica, additional, Shane, Emily, additional, Copeland, Charles D., additional, Ragel, Lauren, additional, Georgousakis, Charidimos, additional, Lee, Sanghwa, additional, Reynolds, Dawn, additional, Talgo, Avery, additional, Gonzalez, Juan, additional, Zhang, Ling, additional, Rajurkar, Ashish B., additional, Ruiz, Michel, additional, Daniels, Erin, additional, Maree, Liezl, additional, Pariyar, Shree, additional, Busch, Wolfgang, additional, and Pereira, Talmo D., additional

Published
2024
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9. Fast and efficient root phenotyping via pose estimation

Author

Berrigan, Elizabeth M, primary, Wang, Lin, additional, Carrillo, Hannah, additional, Echegoyen, Kimberly, additional, Kappes, Mikayla, additional, Torres, Jorge, additional, Ai-Perreira, Angel, additional, McCoy, Erica, additional, Shane, Emily, additional, Copeland, Charles D, additional, Ragel, Lauren, additional, Georgousakis, Charidimos, additional, Lee, Sanghwa, additional, Reynolds, Dawn, additional, Talgo, Avery, additional, Gonzalez, Juan, additional, Zhang, Ling, additional, Rajurkar, Ashish B, additional, Ruiz, Michel, additional, Daniels, Erin, additional, Maree, Liezl, additional, Pariyar, Shree, additional, Busch, Wolfgang, additional, and Pereira, Talmo D, additional

Published
2023
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10. Root Phenotyping Using Pose Estimation

Author

Berrigan, Elizabeth Marie, primary, Wang, Lin, additional, Carrillo, Hannah, additional, Echegoyen, Kimberly, additional, Kappes, Mikayla, additional, Torres, Jorge, additional, Ai-Perreira, Angel, additional, McCoy, Erica, additional, Shane, Emily, additional, Copeland, Charles, additional, Ragel, Lauren, additional, Georgousakis, Charidimos, additional, Lee, Sanghwa, additional, Reynolds, Dawn, additional, Talgo, Avery, additional, Gonzalez, Juan, additional, Zhang, Ling, additional, Rajurkar, Ashish, additional, Ruiz, Michel, additional, Daniels, Erin, additional, Maree, Liezl, additional, Pariyar, Shree, additional, Busch, Wolfgang, additional, and Pereira, Talmo D., additional

Published
2023
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12. Gibberellic Acid Alleviates Cadmium-Induced Seed Germination Inhibition through Modulation of Carbohydrate Metabolism and Antioxidant Capacity in Mung Bean Seedlings

Author

Kaur, Harmanjit, primary, Nazir, Faroza, additional, Hussain, Sofi Javed, additional, Kaur, Rupinder, additional, Rajurkar, Ashish B., additional, Kumari, Sarika, additional, Siddiqui, Manzer H., additional, Mahajan, Moksh, additional, Khatoon, Sayeda, additional, and Khan, M. Iqbal R., additional

Published
2023
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13. Identification of Novel Germplasm and Genetic Loci for Enhancing Mineral Element Uptake in Soybean

Author

Shinde, Suhas, primary, Dhingra, Anuradha, additional, D’Agastino, Leonidas, additional, Shinde, Harshraj, additional, Rajurkar, Ashish B., additional, Sonah, Humira, additional, Vuong, Tri D., additional, Siebecker, Matthew G., additional, Hancock, C. Nathan, additional, Nguyen, Henry T., additional, Deshmukh, Rupesh, additional, and Patil, Gunvant B., additional

Published
2023
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14. 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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15. 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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22. Improving drought tolerance in rice: Ensuring food security through multi-dimensional approaches.

Author

Khan, M. Iqbal R., Palakolanu, Sudhakar R., Chopra, Priyanka, Rajurkar, Ashish B., Gupta, Ravi, Iqbal, Noushina, and Maheshwari, Chirag

Subjects
DROUGHT tolerance, CEREALS as food, FOOD security, PHYSIOLOGICAL adaptation, RICE processing, FOOD crops, RICE
Abstract

Drought has been highly prevalent around the world especially in Sub-Saharan Africa and South-East Asian countries. Consistent climatic instabilities and unpredictable rainfall patterns are further worsening the situation. Rice is a C3 staple cereal and an important food crop for the majority of the world's population and drought stress is one of the major growth retarding threats for rice that slashes down grain quality and yield. Drought deteriorates rice productivity and induces various acclimation responses that aids in stress mitigation. However, the complexity of traits associated with drought tolerance has made the understanding of drought stress-induced responses in rice a challenging process. An integrative understanding based on physiological adaptations, omics, transgenic and molecular breeding approaches successively backed up to developing drought stress-tolerant rice. The review represents a step forward to develop drought-resilient rice plants by exploiting the knowledge that collaborates with omics-based developments with integrative efforts to ensure the compilation of all the possible strategies undertaken to develop drought stresstolerant rice. [ABSTRACT FROM AUTHOR]

Published
2021
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24. In vitro screening and molecular genetic markers associated with salt tolerance in maize

Author

Shende Sarika Shankarrao and Rajurkar Ashish Balkrishna

Subjects
Genetics, Marker-assisted selection, Biology, Applied Microbiology and Biotechnology, RAPD, Salinity, chemistry.chemical_compound, chemistry, Genetic marker, Molecular marker, Callus, Genotype, Agronomy and Crop Science, Molecular Biology, DNA, Biotechnology
Abstract

Salinity is one of the major limitations of plant growth and productivity all over the world. In vitro screening with marker assisted selection can greatly facilitate the selection and isolation of useful tolerant lines. The genetic basis of salt tolerance was examined in salt tolerant (EC 558706), moderately tolerant (EC 558719) and salt sensitive (EC 558620) maize genotypes. Callus lines from mature embryos were developed and exposed to culture media containing, 0.5, 1, 1.5 and 2% NaCl concentrations. The treated calli were used to detect DNA based molecular markers associated with salt tolerance by random amplification of polymorphic DNA (RAPD) technique. Ten (10) polymorphic bands were found related to salt tolerance in maize. The analysis revealed A 11 (1200, 600, 300 bp), C 20 (300 bp), OPA 10 (2800 bp), OPA 13 (900, 600, 250 bp), OPX 11 (800 bp), OPI 01 (700 bp) and OPK 20 (700 bp) primers associated with salt tolerance which can be utilized in breeding programmevia., marker assisted selection and developing salt tolerant genotype by genetic transformation. Key words: Maize, salt tolerance, molecular marker, RAPD.

Published
2013

26. Fast and efficient root phenotyping via pose estimation.

Author

Berrigan EM, Wang L, Carrillo H, Echegoyen K, Kappes M, Torres J, Ai-Perreira A, McCoy E, Shane E, Copeland CD, Ragel L, Georgousakis C, Lee S, Reynolds D, Talgo A, Gonzalez J, Zhang L, Rajurkar AB, Ruiz M, Daniels E, Maree L, Pariyar S, Busch W, and Pereira TD

Abstract

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/., Competing Interests: Competing interests: W.B. is a co-founder of Cquesta, a company that works on crop root growth and carbon sequestration.

Published
2023
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