1
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Song Y, Sapes G, Chang S, Chowdhry R, Mejia T, Hampton A, Kucharski S, Sazzad TMS, Zhang Y, Tillman BL, Resende MFR, Koppal S, Wilson C, Gerber S, Zare A, Hammond WM. Hyperspectral signals in the soil: Plant-soil hydraulic connection and disequilibrium as mechanisms of drought tolerance and rapid recovery. PLANT, CELL & ENVIRONMENT 2024. [PMID: 38924477 DOI: 10.1111/pce.15011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/12/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024]
Abstract
Predicting soil water status remotely is appealing due to its low cost and large-scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre-dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought-induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (-5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species-specific considerations in soil water status predictions from canopy reflectance.
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Affiliation(s)
- Yangyang Song
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Gerard Sapes
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Spencer Chang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Ritesh Chowdhry
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Tomas Mejia
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Anna Hampton
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Shelby Kucharski
- School of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA
| | - T M Shahiar Sazzad
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Yuxuan Zhang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Barry L Tillman
- North Florida Research and Education Center, University of Florida, Marianna, Florida, USA
| | - Márcio F R Resende
- Horticultural Sciences Department, University of Florida, Gainesville, Florida, USA
| | - Sanjeev Koppal
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - Chris Wilson
- Agronomy Department, University of Florida, Gainesville, Florida, USA
| | - Stefan Gerber
- Soil, Water and Ecosystem Sciences Department, University of Florida, Gainesville, Florida, USA
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, USA
| | - William M Hammond
- Agronomy Department, University of Florida, Gainesville, Florida, USA
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2
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Sordo Z, Andeer P, Sethian J, Northen T, Ushizima D. RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs. Sci Rep 2024; 14:12907. [PMID: 38839814 PMCID: PMC11153571 DOI: 10.1038/s41598-024-63497-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 05/29/2024] [Indexed: 06/07/2024] Open
Abstract
Flatbed scanners are commonly used for root analysis, but typical manual segmentation methods are time-consuming and prone to errors, especially in large-scale, multi-plant studies. Furthermore, the complex nature of root structures combined with noisy backgrounds in images complicates automated analysis. Addressing these challenges, this article introduces RhizoNet, a deep learning-based workflow to semantically segment plant root scans. Utilizing a sophisticated Residual U-Net architecture, RhizoNet enhances prediction accuracy and employs a convex hull operation for delineation of the primary root component. Its main objective is to accurately segment root biomass and monitor its growth over time. RhizoNet processes color scans of plants grown in a hydroponic system known as EcoFAB, subjected to specific nutritional treatments. The root detection model using RhizoNet demonstrates strong generalization in the validation tests of all experiments despite variable treatments. The main contributions are the standardization of root segmentation and phenotyping, systematic and accelerated analysis of thousands of images, significantly aiding in the precise assessment of root growth dynamics under varying plant conditions, and offering a path toward self-driving labs.
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Affiliation(s)
- Zineb Sordo
- Computing Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94760, USA
| | - Peter Andeer
- Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94760, USA
| | - James Sethian
- Computing Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94760, USA
- Department of Mathematics, University of California, Berkeley, CA, 94720, USA
| | - Trent Northen
- Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94760, USA
| | - Daniela Ushizima
- Computing Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, 94760, USA.
- Berkeley Institute for Data Science, University of California, Berkeley, CA, 94760, USA.
- Bakar Institute, University of California, San Francisco, CA, 94143, USA.
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3
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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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4
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Lescano MR, Macagno J, Berli CLA. Model-Based Analysis of Lactuca sativa Root Growth under the Action of Herbicides in Milli-Channel Arrays with In Situ Imaging. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:13255-13262. [PMID: 37651710 DOI: 10.1021/acs.jafc.3c04105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Extracting practical information from the large amounts of data gathered during the live imaging analysis of plant organs is a challenging issue. The present work investigates the use of the logistic growth model to analyze experimental data from root elongation assays performed in milli-fluidic devices with in situ imaging. Lactuca sativa was used as a bioindicator and was subjected to wide concentration ranges of four different herbicides: 2,4-D, atrazine, glyphosate, and paraquat. The model parameters were directly connected to standard indicators of toxicity and plant development, such as the LD50 and the absolute growth rate, respectively. In addition, it was found that realistic predictions of the maximum root length can be achieved about 60 h before the bioassay end point, which could significantly shorten the turnaround time. The combination of milli-fluidic devices, real-time imaging, and model-based data analysis becomes a powerful tool for environmental studies and ecotoxicity testing.
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Affiliation(s)
- Maia R Lescano
- INTEC (Universidad Nacional del Litoral-CONICET), Predio CCT CONICET Santa Fe, RN 168, Santa Fe 3000, Argentina
| | - Joana Macagno
- INTEC (Universidad Nacional del Litoral-CONICET), Predio CCT CONICET Santa Fe, RN 168, Santa Fe 3000, Argentina
| | - Claudio L A Berli
- INTEC (Universidad Nacional del Litoral-CONICET), Predio CCT CONICET Santa Fe, RN 168, Santa Fe 3000, Argentina
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5
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Narisetti N, Awais M, Khan M, Stolzenburg F, Stein N, Gladilin E. Awn Image Analysis and Phenotyping Using BarbNet. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0081. [PMID: 38235124 PMCID: PMC10794052 DOI: 10.34133/plantphenomics.0081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/23/2023] [Indexed: 01/19/2024]
Abstract
Consideration of the properties of awns is important for the phenotypic description of grain crops. Awns have a number of important functions in grasses, including assimilation, mechanical protection, and seed dispersal and burial. An important feature of the awn is the presence or absence of barbs-tiny hook-like single-celled trichomes on the outer awn surface that can be visualized using microscopic imaging. There are, however, no suitable software tools for the automated analysis of these small, semi-transparent structures in a high-throughput manner. Furthermore, automated analysis of barbs using conventional methods of pattern detection and segmentation is hampered by high variability of their optical appearance including size, shape, and surface density. In this work, we present a software tool for automated detection and phenotyping of barbs in microscopic images of awns, which is based on a dedicated deep learning model (BarbNet). Our experimental results show that BarbNet is capable of detecting barb structures in different awn phenotypes with an average accuracy of 90%. Furthermore, we demonstrate that phenotypic traits derived from BarbNet-segmented images enable a quite robust categorization of 4 contrasting awn phenotypes with an accuracy of >85%. Based on the promising results of this work, we see that the proposed model has potential applications in the automation of barley awns sorting for plant developmental analysis.
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Affiliation(s)
- Narendra Narisetti
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Molecular Genetics, 06466 Seeland, Germany
| | - Muhammad Awais
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Genebank, 06466 Seeland, Germany
| | - Muhammad Khan
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Genebank, 06466 Seeland, Germany
| | - Frieder Stolzenburg
- Harz University of Applied Sciences, Automation and Computer Sciences Department, 38855 Wernigerode, Germany
| | - Nils Stein
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Genebank, 06466 Seeland, Germany
- Center of integrated Breeding Research (CiBreed), Department of Crop Sciences, Georg-August-University, 37075 Göttingen, Germany
| | - Evgeny Gladilin
- Leibniz Institute for Plant Genetics and Crop Plant Research (IPK), Molecular Genetics, 06466 Seeland, Germany
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6
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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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7
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As good as human experts in detecting plant roots in minirhizotron images but efficient and reproducible: the convolutional neural network "RootDetector". Sci Rep 2023; 13:1399. [PMID: 36697423 PMCID: PMC9876992 DOI: 10.1038/s41598-023-28400-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
Plant roots influence many ecological and biogeochemical processes, such as carbon, water and nutrient cycling. Because of difficult accessibility, knowledge on plant root growth dynamics in field conditions, however, is fragmentary at best. Minirhizotrons, i.e. transparent tubes placed in the substrate into which specialized cameras or circular scanners are inserted, facilitate the capture of high-resolution images of root dynamics at the soil-tube interface with little to no disturbance after the initial installation. Their use, especially in field studies with multiple species and heterogeneous substrates, though, is limited by the amount of work that subsequent manual tracing of roots in the images requires. Furthermore, the reproducibility and objectivity of manual root detection is questionable. Here, we use a Convolutional Neural Network (CNN) for the automatic detection of roots in minirhizotron images and compare the performance of our RootDetector with human analysts with different levels of expertise. Our minirhizotron data come from various wetlands on organic soils, i.e. highly heterogeneous substrates consisting of dead plant material, often times mainly roots, in various degrees of decomposition. This may be seen as one of the most challenging soil types for root segmentation in minirhizotron images. RootDetector showed a high capability to correctly segment root pixels in minirhizotron images from field observations (F1 = 0.6044; r2 compared to a human expert = 0.99). Reproducibility among humans, however, depended strongly on expertise level, with novices showing drastic variation among individual analysts and annotating on average more than 13-times higher root length/cm2 per image compared to expert analysts. CNNs such as RootDetector provide a reliable and efficient method for the detection of roots and root length in minirhizotron images even from challenging field conditions. Analyses with RootDetector thus save resources, are reproducible and objective, and are as accurate as manual analyses performed by human experts.
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8
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Becker C, Berthomé R, Delavault P, Flutre T, Fréville H, Gibot-Leclerc S, Le Corre V, Morel JB, Moutier N, Muños S, Richard-Molard C, Westwood J, Courty PE, de Saint Germain A, Louarn G, Roux F. The ecologically relevant genetics of plant-plant interactions. TRENDS IN PLANT SCIENCE 2023; 28:31-42. [PMID: 36114125 DOI: 10.1016/j.tplants.2022.08.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 08/03/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
Interactions among plants have been long recognized as a major force driving plant community dynamics and crop yield. Surprisingly, our knowledge of the ecological genetics associated with variation of plant-plant interactions remains limited. In this opinion article by scientists from complementary disciplines, the international PLANTCOM network identified four timely questions to foster a better understanding of the mechanisms mediating plant assemblages. We propose that by identifying the key relationships among phenotypic traits involved in plant-plant interactions and the underlying adaptive genetic and molecular pathways, while considering environmental fluctuations at diverse spatial and time scales, we can improve predictions of genotype-by-genotype-by-environment interactions and modeling of productive and stable plant assemblages in wild habitats and crop fields.
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Affiliation(s)
- Claude Becker
- Genetics, Faculty of Biology, Ludwig Maximilians-University, 82152 Martinsried, Germany
| | - Richard Berthomé
- LIPME, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | | | - Timothée Flutre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Hélène Fréville
- AGAP, Université Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Stéphanie Gibot-Leclerc
- Agroécologie, INRAE, Institut Agro, Université du Bourgogne, Université Bourgogne-Franche-Comté, F-21000 Dijon, France
| | - Valérie Le Corre
- Agroécologie, INRAE, Institut Agro, Université du Bourgogne, Université Bourgogne-Franche-Comté, F-21000 Dijon, France
| | - Jean-Benoit Morel
- PHIM Plant Health Institute, Université Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - Nathalie Moutier
- Institute for Genetics, Environment and Plant Protection (IGEPP), INRAE, Institut Agro, Université Rennes 1, 35650 Le Rheu, France
| | - Stéphane Muños
- LIPME, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France
| | - Céline Richard-Molard
- Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, 78850 Thiverval-Grignon, France
| | - James Westwood
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA
| | - Pierre-Emmanuel Courty
- Agroécologie, INRAE, Institut Agro, Université du Bourgogne, Université Bourgogne-Franche-Comté, F-21000 Dijon, France
| | - Alexandre de Saint Germain
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin (IJPB), 78000 Versailles, France
| | | | - Fabrice Roux
- LIPME, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, France.
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9
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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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10
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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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11
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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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