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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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2
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Weihs BJ, Heuschele DJ, Tang Z, York LM, Zhang Z, Xu Z. The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0178. [PMID: 38711621 PMCID: PMC11070851 DOI: 10.34133/plantphenomics.0178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 03/27/2024] [Indexed: 05/08/2024]
Abstract
Roots are essential for acquiring water and nutrients to sustain and support plant growth and anchorage. However, they have been studied less than the aboveground traits in phenotyping and plant breeding until recent decades. In modern times, root properties such as morphology and root system architecture (RSA) have been recognized as increasingly important traits for creating more and higher quality food in the "Second Green Revolution". To address the paucity in RSA and other root research, new technologies are being investigated to fill the increasing demand to improve plants via root traits and overcome currently stagnated genetic progress in stable yields. Artificial intelligence (AI) is now a cutting-edge technology proving to be highly successful in many applications, such as crop science and genetic research to improve crop traits. A burgeoning field in crop science is the application of AI to high-resolution imagery in analyses that aim to answer questions related to crops and to better and more speedily breed desired plant traits such as RSA into new cultivars. This review is a synopsis concerning the origins, applications, challenges, and future directions of RSA research regarding image analyses using AI.
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Affiliation(s)
- Brandon J. Weihs
- United States Department of Agriculture–Agricultural Research Service–Plant Science Research, St. Paul, MN 55108, USA
- Department of Agronomy and Plant Genetics,
University of Minnesota, St. Paul, MN, 55108, USA
| | - Deborah-Jo Heuschele
- United States Department of Agriculture–Agricultural Research Service–Plant Science Research, St. Paul, MN 55108, USA
- Department of Agronomy and Plant Genetics,
University of Minnesota, St. Paul, MN, 55108, USA
| | - Zhou Tang
- Department of Crop and Soil Sciences,
Washington State University, Pullman, WA 99164, USA
| | - Larry M. York
- Biosciences Division and Center for Bioenergy Innovation,
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences,
Washington State University, Pullman, WA 99164, USA
| | - Zhanyou Xu
- United States Department of Agriculture–Agricultural Research Service–Plant Science Research, St. Paul, MN 55108, USA
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3
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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, Pereira TD. Fast and Efficient Root Phenotyping via Pose Estimation. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0175. [PMID: 38629082 PMCID: PMC11020144 DOI: 10.34133/plantphenomics.0175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 03/20/2024] [Indexed: 04/19/2024]
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 that 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 pose-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/.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wolfgang Busch
- Salk Institute for Biological Studies, La Jolla, CA 92037, USA
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4
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Niño de Rivera A, Jawdy S, Chen JG, Feng K, Yates TB, Tuskan GA, Muchero W, Fuxin L, Strauss SH. GWAS supported by computer vision identifies large numbers of candidate regulators of in planta regeneration in Populus trichocarpa. G3 (BETHESDA, MD.) 2024; 14:jkae026. [PMID: 38325329 PMCID: PMC10989874 DOI: 10.1093/g3journal/jkae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/18/2024] [Accepted: 01/20/2024] [Indexed: 02/09/2024]
Abstract
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Yuan Jiang
- Statistics Department, Oregon State University, 239 Weniger Hall, Corvallis, OR 97331, USA
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
| | - Sara Jawdy
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Kai Feng
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Timothy B Yates
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research, University of Tennessee-Knoxville, 310 Ferris Hall 1508 Middle Dr, Knoxville, TN 37996, USA
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 1148 Kelley Engineering Center, Corvallis, OR 97331, USA
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97311, USA
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Ohlsson JA, Leong JX, Elander PH, Ballhaus F, Holla S, Dauphinee AN, Johansson J, Lommel M, Hofmann G, Betnér S, Sandgren M, Schumacher K, Bozhkov PV, Minina EA. SPIRO - the automated Petri plate imaging platform designed by biologists, for biologists. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 118:584-600. [PMID: 38141174 DOI: 10.1111/tpj.16587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/04/2023] [Indexed: 12/25/2023]
Abstract
Phenotyping of model organisms grown on Petri plates is often carried out manually, despite the procedures being time-consuming and laborious. The main reason for this is the limited availability of automated phenotyping facilities, whereas constructing a custom automated solution can be a daunting task for biologists. Here, we describe SPIRO, the Smart Plate Imaging Robot, an automated platform that acquires time-lapse photographs of up to four vertically oriented Petri plates in a single experiment, corresponding to 192 seedlings for a typical root growth assay and up to 2500 seeds for a germination assay. SPIRO is catered specifically to biologists' needs, requiring no engineering or programming expertise for assembly and operation. Its small footprint is optimized for standard incubators, the inbuilt green LED enables imaging under dark conditions, and remote control provides access to the data without interfering with sample growth. SPIRO's excellent image quality is suitable for automated image processing, which we demonstrate on the example of seed germination and root growth assays. Furthermore, the robot can be easily customized for specific uses, as all information about SPIRO is released under open-source licenses. Importantly, uninterrupted imaging allows considerably more precise assessment of seed germination parameters and root growth rates compared with manual assays. Moreover, SPIRO enables previously technically challenging assays such as phenotyping in the dark. We illustrate the benefits of SPIRO in proof-of-concept experiments which yielded a novel insight on the interplay between autophagy, nitrogen sensing, and photoblastic response.
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Affiliation(s)
- Jonas A Ohlsson
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Jia Xuan Leong
- Department of Algal Development and Evolution, Max Planck Institute for Biology Tübingen, Tübingen, 72076, Germany
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Auf der Morgenstelle 32, Tübingen, D-72076, Germany
| | - Pernilla H Elander
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Florentine Ballhaus
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Sanjana Holla
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Adrian N Dauphinee
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | | | - Mark Lommel
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
- Department of Microbiology, Saarland University, Campus A1.5, Saarbrücken, 66123, Germany
| | - Gero Hofmann
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
| | - Staffan Betnér
- Northern Registry Centre, Department of Public Health and Clinical Medicine, Umeå University, Umeå, 90187, Sweden
| | - Mats Sandgren
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Karin Schumacher
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
| | - Peter V Bozhkov
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
| | - Elena A Minina
- Department of Molecular Sciences, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Uppsala, SE-750 07, Sweden
- Centre for Organismal Studies (COS), Heidelberg University, Im Neuenheimer Feld 230, Heidelberg, 69120, Germany
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6
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Sarkar S, Ganapathysubramanian B, Singh A, Fotouhi F, Kar S, Nagasubramanian K, Chowdhary G, Das SK, Kantor G, Krishnamurthy A, Merchant N, Singh AK. Cyber-agricultural systems for crop breeding and sustainable production. TRENDS IN PLANT SCIENCE 2024; 29:130-149. [PMID: 37648631 DOI: 10.1016/j.tplants.2023.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/19/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023]
Abstract
The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS - sensing, modeling, and actuation - and the emerging concept of agricultural digital twins (DTs). We also discuss how scalable CI is becoming a key enabler of smart agriculture. In this review we shed light on the significance of CAS in revolutionizing crop breeding and production by enhancing efficiency, productivity, sustainability, and resilience to changing climate. Finally, we identify underexplored and promising future directions for CAS research and development.
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Affiliation(s)
- Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA.
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Fateme Fotouhi
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA; Department of Computer Science, Iowa State University, Ames, IA, USA
| | | | | | - Girish Chowdhary
- Department of Agricultural and Biological Engineering and Department of Computer Science, University of Illinois at Urbana Champaign, Champaign, Urbana, IL, USA
| | - Sajal K Das
- Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA
| | - George Kantor
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, AZ, USA
| | - Asheesh K Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA.
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7
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Khoroshevsky F, Zhou K, Chemweno S, Edan Y, Bar-Hillel A, Hadar O, Rewald B, Baykalov P, Ephrath JE, Lazarovitch N. Automatic Root Length Estimation from Images Acquired In Situ without Segmentation. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0132. [PMID: 38230354 PMCID: PMC10790720 DOI: 10.34133/plantphenomics.0132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/12/2023] [Indexed: 01/18/2024]
Abstract
Image-based root phenotyping technologies, including the minirhizotron (MR), have expanded our understanding of the in situ root responses to changing environmental conditions. The conventional manual methods used to analyze MR images are time-consuming, limiting their implementation. This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total (cumulative) root length (TRL) per MR image without requiring segmentation. Training data were derived from manual annotations in Rootfly, commonly used software for MR image analysis. We compared TRL estimation with 2 models, a regression-based model and a detection-based model that detects the annotated points along the roots. Notably, the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images. The models were trained and tested with 4,015 images acquired using 2 MR system types (manual and automated) and from 4 crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. These datasets are made publicly available as part of this publication. The coefficients of determination (R2), between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets, demonstrating that this tool is accurate and robust. Additional analyses were conducted to examine the effects of (a) the data acquisition system and thus the image quality on the models' performance, (b) automated differentiation between images with and without roots, and (c) the use of the transfer learning technique. These approaches can support precision agriculture by providing real-time root growth information.
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Affiliation(s)
- Faina Khoroshevsky
- Department of Industrial Engineering and Management,
Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Kaining Zhou
- The Jacob Blaustein Center for Scientific Cooperation,
The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
- French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
| | - Sharon Chemweno
- The Albert Katz International School for Desert Studies,
The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
- French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
| | - Yael Edan
- Department of Industrial Engineering and Management,
Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Aharon Bar-Hillel
- Department of Industrial Engineering and Management,
Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Ofer Hadar
- Department of Communication Systems Engineering, School of Electrical and Computer Engineering,
Ben-Gurion University of the Negev, Beer Sheva, Israel
| | - Boris Rewald
- Institute of Forest Ecology, Department of Forest and Soil Sciences,
University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
- Faculty of Forestry and Wood Technology,
Mendel University in Brno, Brno, Czech Republic
| | - Pavel Baykalov
- Institute of Forest Ecology, Department of Forest and Soil Sciences,
University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
- Vienna Scientific Instruments GmbH, Alland, Austria
| | - Jhonathan E. Ephrath
- French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
| | - Naftali Lazarovitch
- French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer, Israel
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8
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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, Pereira TD. Fast and efficient root phenotyping via pose estimation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.20.567949. [PMID: 38045278 PMCID: PMC10690188 DOI: 10.1101/2023.11.20.567949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
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/.
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Affiliation(s)
| | - Lin Wang
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Hannah Carrillo
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Kimberly Echegoyen
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Mikayla Kappes
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Jorge Torres
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Angel Ai-Perreira
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Erica McCoy
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Emily Shane
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Charles D. Copeland
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Lauren Ragel
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | | | - Sanghwa Lee
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Dawn Reynolds
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Avery Talgo
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Juan Gonzalez
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Ling Zhang
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Ashish B. Rajurkar
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Michel Ruiz
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Erin Daniels
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Liezl Maree
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Shree Pariyar
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Wolfgang Busch
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
| | - Talmo D. Pereira
- Salk Institute for Biological Studies, La Jolla, CA 92037 United States of America
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9
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Mostafa S, Mondal D, Panjvani K, Kochian L, Stavness I. Explainable deep learning in plant phenotyping. Front Artif Intell 2023; 6:1203546. [PMID: 37795496 PMCID: PMC10546035 DOI: 10.3389/frai.2023.1203546] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/25/2023] [Indexed: 10/06/2023] Open
Abstract
The increasing human population and variable weather conditions, due to climate change, pose a threat to the world's food security. To improve global food security, we need to provide breeders with tools to develop crop cultivars that are more resilient to extreme weather conditions and provide growers with tools to more effectively manage biotic and abiotic stresses in their crops. Plant phenotyping, the measurement of a plant's structural and functional characteristics, has the potential to inform, improve and accelerate both breeders' selections and growers' management decisions. To improve the speed, reliability and scale of plant phenotyping procedures, many researchers have adopted deep learning methods to estimate phenotypic information from images of plants and crops. Despite the successful results of these image-based phenotyping studies, the representations learned by deep learning models remain difficult to interpret, understand, and explain. For this reason, deep learning models are still considered to be black boxes. Explainable AI (XAI) is a promising approach for opening the deep learning model's black box and providing plant scientists with image-based phenotypic information that is interpretable and trustworthy. Although various fields of study have adopted XAI to advance their understanding of deep learning models, it has yet to be well-studied in the context of plant phenotyping research. In this review article, we reviewed existing XAI studies in plant shoot phenotyping, as well as related domains, to help plant researchers understand the benefits of XAI and make it easier for them to integrate XAI into their future studies. An elucidation of the representations within a deep learning model can help researchers explain the model's decisions, relate the features detected by the model to the underlying plant physiology, and enhance the trustworthiness of image-based phenotypic information used in food production systems.
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Affiliation(s)
- Sakib Mostafa
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Debajyoti Mondal
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ian Stavness
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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10
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Nagle MF, Yuan J, Kaur D, Ma C, Peremyslova E, Jiang Y, Zahl B, Niño de Rivera A, Muchero W, Fuxin L, Strauss SH. GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa. HORTICULTURE RESEARCH 2023; 10:uhad125. [PMID: 37560019 PMCID: PMC10407606 DOI: 10.1093/hr/uhad125] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/05/2023] [Indexed: 08/11/2023]
Abstract
Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.
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Affiliation(s)
- Michael F Nagle
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Jialin Yuan
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Damanpreet Kaur
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Cathleen Ma
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Ekaterina Peremyslova
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Yuan Jiang
- Statistics Department, Oregon State University, 103 SW Memorial Place, Corvallis, OR, 97331, United States
| | - Bahiya Zahl
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Alexa Niño de Rivera
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States
- Bredesen Center for Interdisciplinary Research, University of Tennessee, 821 Volunteer Blvd., Knoxville, TN, 37996, United States
| | - Li Fuxin
- Department of Electrical Engineering and Computer Science, Oregon State University, 110 SW Park Terrace, Corvallis, OR, 97331, United States
| | - Steven H Strauss
- Department of Forest Ecosystems and Society, Oregon State University, 3180 SW Jefferson Way, Corvallis, OR, 97331, United States
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11
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Yu Q, Tang H, Zhu L, Zhang W, Liu L, Wang N. A method of cotton root segmentation based on edge devices. FRONTIERS IN PLANT SCIENCE 2023; 14:1122833. [PMID: 36875594 PMCID: PMC9982017 DOI: 10.3389/fpls.2023.1122833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/07/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ for plants to absorb water and nutrients. In situ root research method is an intuitive method to explore root phenotype and its change dynamics. At present, in situ root research, roots can be accurately extracted from in situ root images, but there are still problems such as low analysis efficiency, high acquisition cost, and difficult deployment of image acquisition devices outdoors. Therefore, this study designed a precise extraction method of in situ roots based on semantic segmentation model and edge device deployment. It initially proposes two data expansion methods, pixel by pixel and equal proportion, expand 100 original images to 1600 and 53193 respectively. It then presents an improved DeeplabV3+ root segmentation model based on CBAM and ASPP in series is designed, and the segmentation accuracy is 93.01%. The root phenotype parameters were verified through the Rhizo Vision Explorers platform, and the root length error was 0.669%, and the root diameter error was 1.003%. It afterwards designs a time-saving Fast prediction strategy. Compared with the Normal prediction strategy, the time consumption is reduced by 22.71% on GPU and 36.85% in raspberry pie. It ultimately deploys the model to Raspberry Pie, realizing the low-cost and portable root image acquisition and segmentation, which is conducive to outdoor deployment. In addition, the cost accounting is only $247. It takes 8 hours to perform image acquisition and segmentation tasks, and the power consumption is as low as 0.051kWh. In conclusion, the method proposed in this study has good performance in model accuracy, economic cost, energy consumption, etc. This paper realizes low-cost and high-precision segmentation of in-situ root based on edge equipment, which provides new insights for high-throughput field research and application of in-situ root.
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Affiliation(s)
- Qiushi Yu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Hui Tang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Lingxiao Zhu
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Wenjie Zhang
- College of Modern Science And Technology, Hebei Agricultural University, Baoding, China
| | - Liantao Liu
- College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
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12
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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13
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Kinose R, Utsumi Y, Iwamura M, Kise K. Tiller estimation method using deep neural networks. FRONTIERS IN PLANT SCIENCE 2023; 13:1016507. [PMID: 36714728 PMCID: PMC9880423 DOI: 10.3389/fpls.2022.1016507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 12/19/2022] [Indexed: 06/18/2023]
Abstract
This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most important determinants of yield. Traditionally, the tiller number is usually counted by hand, and so an automated approach is necessary for high-throughput phenotyping. Conventional methods use heuristic features to estimate the tiller number. Based on the successful application of DNNs in the field of computer vision, the use of DNN-based features instead of heuristic features is expected to improve the estimation accuracy. However, as DNNs generally require large volumes of data for training, it is difficult to apply them to estimation problems for which large training datasets are unavailable. In this paper, we use two strategies to overcome the problem of insufficient training data: the use of a pretrained DNN model and the use of pretext tasks for learning the feature representation. We extract features using the resulting DNNs and estimate the tiller numbers through a regression technique. We conducted experiments using side-view whole plant images taken with plan backgroud. The experimental results show that the proposed methods using a pretrained model and specific pretext tasks achieve better performance than the conventional method.
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Affiliation(s)
- Rikuya Kinose
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
| | - Yuzuko Utsumi
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Masakazu Iwamura
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
| | - Koichi Kise
- Graduate School of Engineering, Osaka Prefecture University, Sakai, Japan
- Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan
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14
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Peeples J, Xu W, Gloaguen R, Rowland D, Zare A, Brym Z. Spatial and Texture Analysis of Root System distribution with Earth mover's Distance (STARSEED). PLANT METHODS 2023; 19:2. [PMID: 36604751 PMCID: PMC9814335 DOI: 10.1186/s13007-022-00974-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
PURPOSE Root system architectures are complex and challenging to characterize effectively for agronomic and ecological discovery. METHODS We propose a new method, Spatial and Texture Analysis of Root SystEm distribution with Earth mover's Distance (STARSEED), for comparing root system distributions that incorporates spatial information through a novel application of the Earth Mover's Distance (EMD). RESULTS We illustrate that the approach captures the response of sesame root systems for different genotypes and soil moisture levels. STARSEED provides quantitative and visual insights into changes that occur in root architectures across experimental treatments. CONCLUSION STARSEED can be generalized to other plants and provides insight into root system architecture development and response to varying growth conditions not captured by existing root architecture metrics and models. The code and data for our experiments are publicly available: https://github.com/GatorSense/STARSEED .
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Affiliation(s)
- Joshua Peeples
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, 77845 USA
| | - Weihuang Xu
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, 32611 USA
| | | | - Diane Rowland
- College of Natural Sciences, Forestry, and Agriculture, University of Maine, Orono, 04469 USA
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, 32611 USA
| | - Zachary Brym
- Tropical Research and Education Center, University of Florida, Gainesville, 33031 USA
- Department of Agronomy, University of Florida, Gainesville, 32611 USA
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15
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Pierz LD, Heslinga DR, Buell CR, Haus MJ. An image-based technique for automated root disease severity assessment using PlantCV. APPLICATIONS IN PLANT SCIENCES 2023; 11:e11507. [PMID: 36818784 PMCID: PMC9934521 DOI: 10.1002/aps3.11507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/31/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
PREMISE Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis. METHODS Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot. RESULTS Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output. DISCUSSION Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.
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Affiliation(s)
- Logan D. Pierz
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
| | - Dilyn R. Heslinga
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
| | - C. Robin Buell
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of Crop and Soil SciencesUniversity of GeorgiaAthensGeorgia30602USA
| | - Miranda J. Haus
- Department of Plant BiologyMichigan State UniversityEast LansingMichigan48824USA
- Plant Resilience InstituteMichigan State UniversityEast LansingMichigan48824USA
- Department of HorticultureMichigan State UniversityEast LansingMichigan48824USA
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16
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Fernandez R, Crabos A, Maillard M, Nacry P, Pradal C. High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings. PLANT METHODS 2022; 18:127. [PMID: 36457133 PMCID: PMC9714072 DOI: 10.1186/s13007-022-00960-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
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Affiliation(s)
- Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Amandine Crabos
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Morgan Maillard
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France
| | - Philippe Nacry
- Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France.
| | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- Inria & LIRMM, Univ Montpellier, CNRS, Montpellier, France.
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17
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Guo Z, Yang C, Yang W, Chen G, Jiang Z, Wang B, Zhang J. Panicle Ratio Network: streamlining rice panicle measurement by deep learning with ultra-high-definition aerial images in the field. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:6575-6588. [PMID: 35776094 DOI: 10.1093/jxb/erac294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
The heading date and effective tiller percentage are important traits in rice, and they directly affect plant architecture and yield. Both traits are related to the ratio of the panicle number to the maximum tiller number, referred to as the panicle ratio (PR). In this study, an automatic PR estimation model (PRNet) based on a deep convolutional neural network was developed. Ultra-high-definition unmanned aerial vehicle (UAV) images were collected from cultivated rice varieties planted in 2384 experimental plots in 2019 and 2020 and in a large field in 2021. The determination coefficient between estimated PR and ground-measured PR reached 0.935, and the root mean square error values for the estimations of the heading date and effective tiller percentage were 0.687 d and 4.84%, respectively. Based on the analysis of the results, various factors affecting PR estimation and strategies for improving PR estimation accuracy were investigated. The satisfactory results obtained in this study demonstrate the feasibility of using UAVs and deep learning techniques to replace ground-based manual methods to accurately extract phenotypic information of crop micro targets (such as grains per panicle, panicle flowering, etc.) for rice and potentially for other cereal crops in future research.
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Affiliation(s)
- Ziyue Guo
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China
| | - Chenghai Yang
- Aerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX, USA
| | - Wangnen Yang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Guoxing Chen
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zhao Jiang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China
| | - Botao Wang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China
| | - Jian Zhang
- Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, China
- Key Laboratory of Farmland Conservation in the Middle and Lower Reaches of the Ministry of Agriculture, Wuhan, China
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18
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Abbas M, Abid MA, Meng Z, Abbas M, Wang P, Lu C, Askari M, Akram U, Ye Y, Wei Y, Wang Y, Guo S, Liang C, Zhang R. Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway. PHYSIOLOGIA PLANTARUM 2022; 174:e13787. [PMID: 36169590 DOI: 10.1111/ppl.13787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/12/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.
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Affiliation(s)
- Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Ali Abid
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Manzar Abbas
- School of Agriculture, Forestry and Food Engineering, Yibin University, Yibin, China
| | - Peilin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Muhammad Askari
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Umar Akram
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yulu Ye
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Sandui Guo
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Chengzhen Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
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19
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Johann to Berens P, Schivre G, Theune M, Peter J, Sall SO, Mutterer J, Barneche F, Bourbousse C, Molinier J. Advanced Image Analysis Methods for Automated Segmentation of Subnuclear Chromatin Domains. EPIGENOMES 2022; 6:epigenomes6040034. [PMID: 36278680 PMCID: PMC9624336 DOI: 10.3390/epigenomes6040034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/19/2022] [Accepted: 10/01/2022] [Indexed: 11/07/2022] Open
Abstract
The combination of ever-increasing microscopy resolution with cytogenetical tools allows for detailed analyses of nuclear functional partitioning. However, the need for reliable qualitative and quantitative methodologies to detect and interpret chromatin sub-nuclear organization dynamics is crucial to decipher the underlying molecular processes. Having access to properly automated tools for accurate and fast recognition of complex nuclear structures remains an important issue. Cognitive biases associated with human-based curation or decisions for object segmentation tend to introduce variability and noise into image analysis. Here, we report the development of two complementary segmentation methods, one semi-automated (iCRAQ) and one based on deep learning (Nucl.Eye.D), and their evaluation using a collection of A. thaliana nuclei with contrasted or poorly defined chromatin compartmentalization. Both methods allow for fast, robust and sensitive detection as well as for quantification of subtle nucleus features. Based on these developments, we highlight advantages of semi-automated and deep learning-based analyses applied to plant cytogenetics.
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Affiliation(s)
| | - Geoffrey Schivre
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
- Université Paris-Saclay, 91190 Orsay, France
| | - Marius Theune
- FB 10 / Molekulare Pflanzenphysiologie, Bioenergetik in Photoautotrophen, Universität Kassel, 34127 Kassel, Germany
| | - Jackson Peter
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
| | | | - Jérôme Mutterer
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
| | - Fredy Barneche
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
| | - Clara Bourbousse
- Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, Centre National de la Recherche Scientifique, Inserm, Université PSL, 75230 Paris, France
- Correspondence: (C.B.); (J.M.)
| | - Jean Molinier
- Institut de Biologie Moléculaire des Plantes du CNRS, 67000 Strasbourg, France
- Correspondence: (C.B.); (J.M.)
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20
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Seidenthal K, Panjvani K, Chandnani R, Kochian L, Eramian M. Iterative image segmentation of plant roots for high-throughput phenotyping. Sci Rep 2022; 12:16563. [PMID: 36195610 PMCID: PMC9532414 DOI: 10.1038/s41598-022-19754-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/02/2022] [Indexed: 11/24/2022] Open
Abstract
Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA’s, and performs particularly well in the presence of non-root objects.
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Affiliation(s)
- Kyle Seidenthal
- Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada
| | - Karim Panjvani
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Rahul Chandnani
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Leon Kochian
- Global Institute for Food Security, University of Saskatchewan, 421 Downey Road, Saskatoon, SK, S7N 4L8, Canada
| | - Mark Eramian
- Department of Computer Science, University of Saskatchewan, 110 Science Place, Saskatoon, SK, S7N 5C9, Canada.
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21
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Zaji A, Liu Z, Xiao G, Sangha JS, Ruan Y. A survey on deep learning applications in wheat phenotyping. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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22
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Smith AG, Han E, Petersen J, Olsen NAF, Giese C, Athmann M, Dresbøll DB, Thorup‐Kristensen K. RootPainter: deep learning segmentation of biological images with corrective annotation. THE NEW PHYTOLOGIST 2022; 236:774-791. [PMID: 35851958 PMCID: PMC9804377 DOI: 10.1111/nph.18387] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/30/2022] [Indexed: 05/27/2023]
Abstract
Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.
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Affiliation(s)
- Abraham George Smith
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
- Department of Computer ScienceUniversity of CopenhagenUniversitetsparken 12100CopenhagenDenmark
| | - Eusun Han
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
- CSIRO Agriculture and FoodPO Box 1700CanberraACT2601Australia
| | - Jens Petersen
- Department of Computer ScienceUniversity of CopenhagenUniversitetsparken 12100CopenhagenDenmark
| | - Niels Alvin Faircloth Olsen
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
| | - Christian Giese
- Department of Agroecology and Organic FarmingUniversity of BonnRegina‐Pacis‐Weg 353113BonnGermany
| | - Miriam Athmann
- Department of Organic Farming and Plant ProductionUniversity of KasselNordbahnhofstr. 1aD‐37213WitzenhausenGermany
| | - Dorte Bodin Dresbøll
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
| | - Kristian Thorup‐Kristensen
- Department of Plant and Environmental ScienceUniversity of CopenhagenHøjbakkegårds Alle 13Tåstrup2630Denmark
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23
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Zhao H, Wang N, Sun H, Zhu L, Zhang K, Zhang Y, Zhu J, Li A, Bai Z, Liu X, Dong H, Liu L, Li C. RhizoPot platform: A high-throughput in situ root phenotyping platform with integrated hardware and software. FRONTIERS IN PLANT SCIENCE 2022; 13:1004904. [PMID: 36247541 PMCID: PMC9558169 DOI: 10.3389/fpls.2022.1004904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/15/2022] [Indexed: 06/01/2023]
Abstract
Quantitative analysis of root development is becoming a preferred option in assessing the function of hidden underground roots, especially in studying resistance to abiotic stresses. It can be enhanced by acquiring non-destructive phenotypic information on roots, such as rhizotrons. However, it is challenging to develop high-throughput phenotyping equipment for acquiring and analyzing in situ root images of root development. In this study, the RhizoPot platform, a high-throughput in situ root phenotyping platform integrating plant culture, automatic in situ root image acquisition, and image segmentation, was proposed for quantitative analysis of root development. Plants (1-5) were grown in each RhizoPot, and the growth time depended on the type of plant and the experimental requirements. For example, the growth time of cotton was about 110 days. The imaging control software (RhizoAuto) could automatically and non-destructively image the roots of RhizoPot-cultured plants based on the set time and resolution (50-4800 dpi) and obtain high-resolution (>1200 dpi) images in batches. The improved DeepLabv3+ tool was used for batch processing of root images. The roots were automatically segmented and extracted from the background for analysis of information on radical features using conventional root software (WinRhizo and RhizoVision Explorer). Root morphology, root growth rate, and lifespan analysis were conducted using in situ root images and segmented images. The platform illustrated the dynamic response characteristics of root phenotypes in cotton. In conclusion, the RhizoPot platform has the characteristics of low cost, high-efficiency, and high-throughput, and thus it can effectively monitor the development of plant roots and realize the quantitative analysis of root phenotypes in situ.
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Affiliation(s)
- Hongjuan Zhao
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Hongchun Sun
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Lingxiao Zhu
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Ke Zhang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Yongjiang Zhang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jijie Zhu
- Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China
| | - Anchang Li
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Zhiying Bai
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Xiaoqing Liu
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Hezhong Dong
- Cotton Research Center, Shandong Key Lab for Cotton Culture and Physiology, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Cundong Li
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, China
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24
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Chakraborty SK, Chandel NS, Jat D, Tiwari MK, Rajwade YA, Subeesh A. Deep learning approaches and interventions for futuristic engineering in agriculture. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07744-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Li A, Zhu L, Xu W, Liu L, Teng G. Recent advances in methods for in situ root phenotyping. PeerJ 2022; 10:e13638. [PMID: 35795176 PMCID: PMC9252182 DOI: 10.7717/peerj.13638] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/06/2022] [Indexed: 01/17/2023] Open
Abstract
Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.
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Affiliation(s)
- Anchang Li
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Lingxiao Zhu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Wenjun Xu
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultrual University, Baoding, Hebei, China
| | - Guifa Teng
- School of Information Science and Technology, Hebei Agricultrual University, Baoding, Hebei, China
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26
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Bauer FM, Lärm L, Morandage S, Lobet G, Vanderborght J, Vereecken H, Schnepf A. Development and Validation of a Deep Learning Based Automated Minirhizotron Image Analysis Pipeline. PLANT PHENOMICS 2022; 2022:9758532. [PMID: 35693120 PMCID: PMC9168891 DOI: 10.34133/2022/9758532] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/28/2022]
Abstract
Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.
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Affiliation(s)
- Felix Maximilian Bauer
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Lena Lärm
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Shehan Morandage
- Institute of Soil Science and Land Evaluation, University of Hohenheim, 70559 78 Stuttgart, Germany
| | - Guillaume Lobet
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Jan Vanderborght
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Harry Vereecken
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
| | - Andrea Schnepf
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany
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27
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Yasrab R, Fu Z, Drukker L, Lee LH, Zhao H, Papageorghiou AT, Noble AJ. End-to-end First Trimester Fetal Ultrasound Video Automated CRL and NT Segmentation. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2022; 2022:9761400. [PMID: 36643819 PMCID: PMC7614066 DOI: 10.1109/isbi52829.2022.9761400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hourglass (NHG) based network performs semantic pixel-wise segmentation to extract NT and CRL structures. Our results show that the NHG network is faster (19.52% < GFlops than FCN32) and offers high pixel agreement (mean-IoU=80.74) with expert manual annotations.
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Affiliation(s)
- Robail Yasrab
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Zeyu Fu
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Lior Drukker
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK,Rabin Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Israel
| | - Lok Hin Lee
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - He Zhao
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Alison J. Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
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28
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Du J, Li B, Lu X, Yang X, Guo X, Zhao C. Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components. PLANT METHODS 2022; 18:54. [PMID: 35468831 PMCID: PMC9036747 DOI: 10.1186/s13007-022-00890-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 04/13/2022] [Indexed: 05/09/2023]
Abstract
BACKGROUND Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically. RESULTS In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the "size-free" space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences. CONCLUSIONS The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.
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Affiliation(s)
- Jianjun Du
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Bo Li
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Agro-Biotechnology Research Center, Beijing, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Xiaozeng Yang
- Beijing Key Laboratory of Agricultural Genetic Resources and Biotechnology, Beijing Agro-Biotechnology Research Center, Beijing, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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29
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Lube V, Noyan MA, Przybysz A, Salama K, Blilou I. MultipleXLab: A high-throughput portable live-imaging root phenotyping platform using deep learning and computer vision. PLANT METHODS 2022; 18:38. [PMID: 35346267 PMCID: PMC8958799 DOI: 10.1186/s13007-022-00864-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. RESULTS We developed the MultipleXLab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the MultipleXLab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. CONCLUSION MultipleXLab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
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Affiliation(s)
- Vinicius Lube
- Laboratory of Plant Cell and Developmental Biology (LPCDB), Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | | | - Alexander Przybysz
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Science and Engineering (CEMSE), KAUST, Thuwal, Saudi Arabia
| | - Khaled Salama
- Sensors Lab, Advanced Membranes and Porous Materials Center (AMPMC), Computer, Electrical and Mathematical Science and Engineering (CEMSE), KAUST, Thuwal, Saudi Arabia
| | - Ikram Blilou
- Laboratory of Plant Cell and Developmental Biology (LPCDB), Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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30
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Teramoto S, Uga Y. Improving the efficiency of plant root system phenotyping through digitization and automation. BREEDING SCIENCE 2022; 72:48-55. [PMID: 36045896 PMCID: PMC8987843 DOI: 10.1270/jsbbs.21053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/11/2021] [Indexed: 05/19/2023]
Abstract
Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category.
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Affiliation(s)
- Shota Teramoto
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan
| | - Yusaku Uga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan
- Corresponding author (e-mail: )
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31
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Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
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Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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32
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Barker R, Johns S, Trane R, Gilroy S. Analysis of Plant Root Gravitropism. Methods Mol Biol 2022; 2494:3-16. [PMID: 35467196 DOI: 10.1007/978-1-0716-2297-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gravity is a powerful element in shaping plant development, with gravitropism, the oriented growth response of plant organs to the direction of gravity, leading to each plant's characteristic form both above and below ground. Despite being conceptually simple to follow, monitoring a plant's directional growth responses can become complex as variation arises from both internal developmental cues as well as effects of the environment. In this protocol, we discuss approaches to gravitropism assays, focusing on automated analyses of root responses. For Arabidopsis, we recommend a simple 90° rotation using seedlings that are 5-8 days old. If images are taken at regular intervals and the environmental metadata is recorded during both seedling development and gravitropic assay, these data can be used to reveal quantitative kinetic patterns at distinct stages of the assay. The use of software that analyzes root system parameters and stores this data in the RSML format opens up the possibility for a host of root parameters to be extracted to characterize growth of the primary root and a range of lateral root phenotypes.
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Affiliation(s)
- Richard Barker
- Department of Botany, University of Wisconsin, Madison, WI, USA
| | - Sarah Johns
- Department of Botany, University of Wisconsin, Madison, WI, USA
| | - Ralph Trane
- Department of Statistics, University of Wisconsin, Madison, WI, USA
| | - Simon Gilroy
- Department of Botany, University of Wisconsin, Madison, WI, USA.
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Serre NBC, Fendrych M. ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamics. QUANTITATIVE PLANT BIOLOGY 2022; 3:e9. [PMID: 37077987 PMCID: PMC10095971 DOI: 10.1017/qpb.2022.4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 03/07/2022] [Accepted: 03/30/2022] [Indexed: 05/03/2023]
Abstract
The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism.
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Affiliation(s)
- Nelson B C Serre
- Department of Experimental Plant Biology, Faculty of Sciences, Charles University, Prague, Czech Republic
| | - Matyáš Fendrych
- Department of Experimental Plant Biology, Faculty of Sciences, Charles University, Prague, Czech Republic
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34
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Choi JY, Seo K, Cho JS, Moon KD. Applying convolutional neural networks to assess the external quality of strawberries. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2021.104071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Abstract
High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.
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Kim M, Lee C, Hong S, Kim SL, Baek JH, Kim KH. High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops. Int J Mol Sci 2021; 22:ijms22158266. [PMID: 34361030 PMCID: PMC8347144 DOI: 10.3390/ijms22158266] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/28/2022] Open
Abstract
Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.
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Affiliation(s)
- Minsu Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Chaewon Lee
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Department of Crop Science and Biotechnology, Chonbuk National University, Jeonju 54896, Korea
| | - Subin Hong
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Song Lim Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Jeong-Ho Baek
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
| | - Kyung-Hwan Kim
- National Institute of Agricultural Science, RDA, Wanju 54874, Korea; (M.K.); (C.L.); (S.H.); (S.L.K.); (J.-H.B.)
- Correspondence:
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Jabusch LK, Kim PW, Chiniquy D, Zhao Z, Wang B, Bowen B, Kang AJ, Yoshikuni Y, Deutschbauer AM, Singh AK, Northen TR. Microfabrication of a Chamber for High-Resolution, In Situ Imaging of the Whole Root for Plant-Microbe Interactions. Int J Mol Sci 2021; 22:7880. [PMID: 34360661 PMCID: PMC8348081 DOI: 10.3390/ijms22157880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/17/2021] [Accepted: 07/18/2021] [Indexed: 11/20/2022] Open
Abstract
Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new 'Imaging EcoFAB' and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root-microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.
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Affiliation(s)
- Lauren K. Jabusch
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (L.K.J.); (D.C.); (A.J.K.); (A.M.D.)
| | - Peter W. Kim
- CBRN Defense and Energy Technologies, Sandia National Laboratory, Livermore, CA 94550, USA
| | - Dawn Chiniquy
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (L.K.J.); (D.C.); (A.J.K.); (A.M.D.)
| | - Zhiying Zhao
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (Z.Z.); (B.W.); (B.B.); (Y.Y.)
| | - Bing Wang
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (Z.Z.); (B.W.); (B.B.); (Y.Y.)
| | - Benjamin Bowen
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (Z.Z.); (B.W.); (B.B.); (Y.Y.)
| | - Ashley J. Kang
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (L.K.J.); (D.C.); (A.J.K.); (A.M.D.)
| | - Yasuo Yoshikuni
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (Z.Z.); (B.W.); (B.B.); (Y.Y.)
| | - Adam M. Deutschbauer
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (L.K.J.); (D.C.); (A.J.K.); (A.M.D.)
| | - Anup K. Singh
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA;
| | - Trent R. Northen
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (L.K.J.); (D.C.); (A.J.K.); (A.M.D.)
- Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; (Z.Z.); (B.W.); (B.B.); (Y.Y.)
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Warman C, Fowler JE. Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology. PLANT REPRODUCTION 2021; 34:81-89. [PMID: 33725183 PMCID: PMC8128740 DOI: 10.1007/s00497-021-00407-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 02/15/2021] [Indexed: 05/09/2023]
Abstract
Advances in deep learning are providing a powerful set of image analysis tools that are readily accessible for high-throughput phenotyping applications in plant reproductive biology. High-throughput phenotyping systems are becoming critical for answering biological questions on a large scale. These systems have historically relied on traditional computer vision techniques. However, neural networks and specifically deep learning are rapidly becoming more powerful and easier to implement. Here, we examine how deep learning can drive phenotyping systems and be used to answer fundamental questions in reproductive biology. We describe previous applications of deep learning in the plant sciences, provide general recommendations for applying these methods to the study of plant reproduction, and present a case study in maize ear phenotyping. Finally, we highlight several examples where deep learning has enabled research that was previously out of reach and discuss the future outlook of these methods.
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Affiliation(s)
- Cedar Warman
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA.
- School of Plant Sciences, University of Arizona, Tucson, AZ, USA.
| | - John E Fowler
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
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Abstract
Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.
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40
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High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales. Emerg Top Life Sci 2021; 5:239-248. [DOI: 10.1042/etls20200273] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 02/09/2021] [Accepted: 02/11/2021] [Indexed: 01/12/2023]
Abstract
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.
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41
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Gao J, Westergaard JC, Sundmark EHR, Bagge M, Liljeroth E, Alexandersson E. Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106723] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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42
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Abstract
Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
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43
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Williamson HF, Brettschneider J, Caccamo M, Davey RP, Goble C, Kersey PJ, May S, Morris RJ, Ostler R, Pridmore T, Rawlings C, Studholme D, Tsaftaris SA, Leonelli S. Data management challenges for artificial intelligence in plant and agricultural research. F1000Res 2021; 10:324. [PMID: 36873457 PMCID: PMC9975417 DOI: 10.12688/f1000research.52204.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Artificial Intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful and usable ways to integrate, compare and visualise large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of Machine Learning (AI) which holds much promise for this domain.
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Affiliation(s)
- Hugh F Williamson
- Exeter Centre for the Study of the Life Sciences & Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, UK
| | | | - Mario Caccamo
- NIAB, National Research Institute of Brewing, East Malling, UK
| | | | - Carole Goble
- Department of Computer Science, University of Manchester, Manchester, UK
| | | | - Sean May
- School of Biosciences, University of Nottingham, Loughborough, UK
| | | | - Richard Ostler
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK
| | - Tony Pridmore
- School of Computer Science, University of Nottingham, Nottingham, UK
| | - Chris Rawlings
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpendem, UK
| | | | - Sotirios A Tsaftaris
- Institute of Digital Communications, University of Edinburgh, Edinburgh, UK.,Alan Turing Institute, London, UK
| | - Sabina Leonelli
- Exeter Centre for the Study of the Life Sciences & Institute for Data Science and Artificial Intelligence, University of Exeter, Exeter, UK.,Alan Turing Institute, London, UK
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Hüther P, Schandry N, Jandrasits K, Bezrukov I, Becker C. ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States. THE PLANT CELL 2020; 32:3674-3688. [PMID: 33037149 PMCID: PMC7721323 DOI: 10.1105/tpc.20.00318] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/28/2020] [Accepted: 10/07/2020] [Indexed: 06/03/2023]
Abstract
Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.
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Affiliation(s)
- Patrick Hüther
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Niklas Schandry
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), 1030 Vienna, Austria
- Genetics, Faculty of Biology, Ludwig-Maximilians-University München, 82152 Martinsried, Germany
| | - Katharina Jandrasits
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Ilja Bezrukov
- Department of Molecular Biology, Max Planck Institute of Developmental Biology, 72076 Tübingen, Germany
| | - Claude Becker
- Gregor Mendel Institute of Molecular Plant Biology (GMI), Austrian Academy of Sciences, Vienna BioCenter (VBC), 1030 Vienna, Austria
- Genetics, Faculty of Biology, Ludwig-Maximilians-University München, 82152 Martinsried, Germany
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Colmer J, O'Neill CM, Wells R, Bostrom A, Reynolds D, Websdale D, Shiralagi G, Lu W, Lou Q, Le Cornu T, Ball J, Renema J, Flores Andaluz G, Benjamins R, Penfield S, Zhou J. SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination. THE NEW PHYTOLOGIST 2020; 228:778-793. [PMID: 32533857 DOI: 10.1111/nph.16736] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 05/25/2020] [Indexed: 05/26/2023]
Abstract
Efficient seed germination and establishment are important traits for field and glasshouse crops. Large-scale germination experiments are laborious and prone to observer errors, leading to the necessity for automated methods. We experimented with five crop species, including tomato, pepper, Brassica, barley, and maize, and concluded an approach for large-scale germination scoring. Here, we present the SeedGerm system, which combines cost-effective hardware and open-source software for seed germination experiments, automated seed imaging, and machine-learning based phenotypic analysis. The software can process multiple image series simultaneously and produce reliable analysis of germination- and establishment-related traits, in both comma-separated values (CSV) and processed images (PNG) formats. In this article, we describe the hardware and software design in detail. We also demonstrate that SeedGerm could match specialists' scoring of radicle emergence. Germination curves were produced based on seed-level germination timing and rates rather than a fitted curve. In particular, by scoring germination across a diverse panel of Brassica napus varieties, SeedGerm implicates a gene important in abscisic acid (ABA) signalling in seeds. We compared SeedGerm with existing methods and concluded that it could have wide utilities in large-scale seed phenotyping and testing, for both research and routine seed technology applications.
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Affiliation(s)
- Joshua Colmer
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Carmel M O'Neill
- Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Rachel Wells
- Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Aaron Bostrom
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Daniel Reynolds
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Danny Websdale
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Gagan Shiralagi
- Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Wei Lu
- College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China
| | - Qiaojun Lou
- Shanghai Agrobiological Gene Center, Shanghai Academy of Agricultural Sciences, Shanghai, 201106, China
| | - Thomas Le Cornu
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Joshua Ball
- Engineering biology, Earlham Institute, Norwich Research Park, Norwich, NR4 7UZ, UK
| | - Jim Renema
- Syngenta Seeds BV, Enkhuizen, 1601 BK, the Netherlands
| | | | | | - Steven Penfield
- Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, UK
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Plant Phenomics Research Center, Jiangsu Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing, 210095, China
- Cambridge Crop Research, National Institute of Agricultural Botany, Cambridge, CB3 0LE, UK
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Abstract
Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.
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47
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Bagley SA, Atkinson JA, Hunt H, Wilson MH, Pridmore TP, Wells DM. Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3319. [PMID: 32545168 PMCID: PMC7309146 DOI: 10.3390/s20113319] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/05/2020] [Accepted: 06/09/2020] [Indexed: 11/17/2022]
Abstract
High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of "affordable phenotyping" solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
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Affiliation(s)
- Stuart A. Bagley
- Integrated Phenomics Group, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK;
| | - Jonathan A. Atkinson
- Future Food Beacon, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK; (J.A.A.); (M.H.W.)
| | - Henry Hunt
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK; (H.H.); (T.P.P.)
| | - Michael H. Wilson
- Future Food Beacon, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK; (J.A.A.); (M.H.W.)
| | - Tony P. Pridmore
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK; (H.H.); (T.P.P.)
| | - Darren M. Wells
- Integrated Phenomics Group, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Sutton Bonington LE12 5RD, UK;
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Jiang Y, Li C. Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:4152816. [PMID: 33313554 PMCID: PMC7706326 DOI: 10.34133/2020/4152816] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/12/2020] [Indexed: 05/19/2023]
Abstract
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
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Affiliation(s)
- Yu Jiang
- Horticulture Section, School of Integrative Plant Science, Cornell AgriTech, Cornell University, USA
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, USA
- Phenomics and Plant Robotics Center, The University of Georgia, USA
| | - Changying Li
- School of Electrical and Computer Engineering, College of Engineering, The University of Georgia, USA
- Phenomics and Plant Robotics Center, The University of Georgia, USA
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Smith AG, Petersen J, Selvan R, Rasmussen CR. Segmentation of roots in soil with U-Net. PLANT METHODS 2020; 16:13. [PMID: 32055251 PMCID: PMC7007677 DOI: 10.1186/s13007-020-0563-0] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 01/27/2020] [Indexed: 05/16/2023]
Abstract
BACKGROUND Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. RESULTS Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an r 2 of 0.9217. We also achieve an F 1 of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image. CONCLUSION We have demonstrated the feasibility of a U-Net based CNN system for segmenting images of roots in soil and for replacing the manual line-intersect method. The success of our approach is also a demonstration of the feasibility of deep learning in practice for small research groups needing to create their own custom labelled dataset from scratch.
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Affiliation(s)
- Abraham George Smith
- Department of Plant and Environmental Sciences, University of Copenhagen, Højbakkegaard Allé 13, 2630 Taastrup, Denmark
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark
| | - Jens Petersen
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen Ø, Denmark
| | - Camilla Ruø Rasmussen
- Department of Plant and Environmental Sciences, University of Copenhagen, Højbakkegaard Allé 13, 2630 Taastrup, Denmark
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Shen C, Liu L, Zhu L, Kang J, Wang N, Shao L. High-Throughput in situ Root Image Segmentation Based on the Improved DeepLabv3+ Method. FRONTIERS IN PLANT SCIENCE 2020; 11:576791. [PMID: 33193519 PMCID: PMC7604297 DOI: 10.3389/fpls.2020.576791] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 09/22/2020] [Indexed: 05/19/2023]
Abstract
The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using in situ images of cotton roots obtained with a micro root window root system monitoring system. Segmentation performance of the proposed method utilizing WinRHIZO Tron MF analysis was assessed using these images. After 80 epochs of training, the final verification set F1-score, recall, and precision were 0.9773, 0.9847, and 0.9702, respectively. The Spearman rank correlation between the manually obtained Rhizotrons manual segmentation root length and automated root length was 0.9667 (p < 10-8), with r 2 = 0.9449. Based on the comparison of our segmentation results with those of traditional manual and U-net segmentation methods, this novel method can more accurately segment root systems in complex soil environments. Thus, using the improved DeepLabv3+ to segment root systems based on micro-root images is an effective method for accurately and quickly segmenting root systems in a homogeneous soil environment and has clear advantages over traditional manual segmentation.
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Affiliation(s)
- Chen Shen
- State Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Liantao Liu
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Lingxiao Zhu
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Crop Growth Regulation of Hebei Province, College of Agronomy, Hebei Agricultural University, Baoding, China
| | - Jia Kang
- State Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
| | - Nan Wang
- State Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
- *Correspondence: Nan Wang,
| | - Limin Shao
- State Key Laboratory of North China Crop Improvement and Regulation, College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China
- Limin Shao,
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