Articles citing this paper
Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis
Christoph Römer A F , Mirwaes Wahabzada B , Agim Ballvora C , Francisco Pinto D , Micol Rossini E , Cinzia Panigada E , Jan Behmann A , Jens Léon C , Christian Thurau B , Christian Bauckhage B , Kristian Kersting B , Uwe Rascher D and Lutz Plümer A
+ Author Affiliations
- Author Affiliations
A Institute of Geodesy and Geoinformation, Geoinformation, University of Bonn, Meckenheimer Allee 172, 53115 Bonn, Germany.
B Institute for Intelligent Analysis and Information Systems, Fraunhofer, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
C Institute of Crop Science and Resource Conservation, Plant Breeding and Biotechnology, University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany.
D Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich, Leo-Brandt-Str., 52425 Jülich, Germany.
E Laboratorio di Telerilevamento delle Dinamiche Ambientali (LTDA), Dip. di Scienze dell’Ambiente e del Territorio (DISAT), Università degli Studi di Milano Bicocca (UNIMIB), Piazza della Scienza, 1, 20126 Milano, Italy.
F Corresponding author. Email: roemer@igg.uni-bonn.de
Functional Plant Biology 39(11) 878-890 https://doi.org/10.1071/FP12060
Submitted: 24 February 2012 Accepted: 8 July 2012 Published: 28 August 2012
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Lohumi Santosh,
Ahmad Khan Haris, Nordon Alison
Computers and Electronics in Agriculture. 2020 178 p.105780
Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods
Yu Keqiang,
Fang Shiyan, Zhao Yanru
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 2021 245 p.118917
In-field hyperspectral imaging: An overview on the ground-based applications in agriculture
Benelli Alessandro,
Cevoli Chiara, Fabbri Angelo
Journal of Agricultural Engineering. 2020 51(3). p.129
Proximal sensing and vegetation indices for site-specific evaluation on an irrigated crop tomato
Marino Stefano, Alvino Arturo
European Journal of Remote Sensing. 2014 47(1). p.271
Recent Advances in Sugarcane Genomics, Physiology, and Phenomics for Superior Agronomic Traits
Meena Mintu Ram,
Appunu Chinnaswamy,
Arun Kumar R.,
Manimekalai R.,
Vasantha S.,
Krishnappa Gopalareddy,
Kumar Ravinder,
Pandey S. K., Hemaprabha G.
Frontiers in Genetics. 2022 13
Close range hyperspectral imaging of plants: A review
Mishra Puneet,
Asaari Mohd Shahrimie Mohd,
Herrero-Langreo Ana,
Lohumi Santosh,
Diezma Belén, Scheunders Paul
Biosystems Engineering. 2017 164 p.49
Using multi-date high spectral resolution data to assess the physiological status of macroscopically undamaged foliage on a regional scale
Kopačková Veronika,
Mišurec Jan,
Lhotáková Zuzana,
Oulehle Filip, Albrechtová Jana
International Journal of Applied Earth Observation and Geoinformation. 2014 27 p.169
Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress
Ryckewaert Maxime,
Gorretta Nathalie,
Henriot Fabienne,
Gobrecht Alexia,
Héran Daphné,
Moura Daniel,
Bendoula Ryad, Roger Jean-Michel
Computers and Electronics in Agriculture. 2021 189 p.106385
The Nature of Societal Conflict in Europe; an Archetypal Analysis of the Postmodern Cosmopolitan, Rural Traditionalist and Urban Precariat
Beugelsdijk Sjoerd,
van Herk Hester, Maseland Robbert
JCMS: Journal of Common Market Studies. 2022 60(6). p.1701
Close-range hyperspectral spectroscopy reveals leaf water content dynamics
Junttila S.,
Hölttä T.,
Saarinen N.,
Kankare V.,
Yrttimaa T.,
Hyyppä J., Vastaranta M.
Remote Sensing of Environment. 2022 277 p.113071
Early detection of water stress in maize based on digital images
Zhuang Shuo,
Wang Ping,
Jiang Boran,
Li Maosong, Gong Zhihong
Computers and Electronics in Agriculture. 2017 140 p.461
Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective
Thomas Stefan,
Kuska Matheus Thomas,
Bohnenkamp David,
Brugger Anna,
Alisaac Elias,
Wahabzada Mirwaes,
Behmann Jan, Mahlein Anne-Katrin
Journal of Plant Diseases and Protection. 2018 125(1). p.5
Biometrics (2017)
Hyperspectral imaging to identify salt-tolerant wheat lines
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II (2017)
Morphological, Physiological, and Genetic Responses to Salt Stress in Alfalfa: A Review
Bhattarai Surendra,
Biswas Dilip,
Fu Yong-Bi, Biligetu Bill
Agronomy. 2020 10(4). p.577
Discriminating Irrigated and Rainfed Maize with Diurnal Fluorescence and Canopy Temperature Airborne Maps
Rossini Micol,
Panigada Cinzia,
Cilia Chiara,
Meroni Michele,
Busetto Lorenzo,
Cogliati Sergio,
Amaducci Stefano, Colombo Roberto
ISPRS International Journal of Geo-Information. 2015 4(2). p.626
On the benefit of topographic dictionaries for detecting disease symptoms on hyperspectral 3D plant models
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (2016)
Eco-friendly Agro-biological Techniques for Enhancing Crop Productivity (2018)
Assessing canopy PRI from airborne imagery to map water stress in maize
Rossini M.,
Fava F.,
Cogliati S.,
Meroni M.,
Marchesi A.,
Panigada C.,
Giardino C.,
Busetto L.,
Migliavacca M.,
Amaducci S., Colombo R.
ISPRS Journal of Photogrammetry and Remote Sensing. 2013 86 p.168
Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat
Bruning Brooke,
Berger Bettina,
Lewis Megan,
Liu Huajian, Garnett Trevor
The Plant Phenome Journal. 2020 3(1).
Detection of the metabolic response to drought stress using hyperspectral reflectance
Burnett Angela C,
Serbin Shawn P,
Davidson Kenneth J,
Ely Kim S,
Rogers Alistair, Gleadow Ros
Journal of Experimental Botany. 2021 72(18). p.6474
Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing
Murphy Mary E.,
Boruff Bryan,
Callow J. Nikolaus, Flower Ken C.
Remote Sensing. 2020 12(3). p.477
Spectral characteristics of leaves diffuse reflection in conditions of soil drought: a study of soft spring wheat cultivars of different drought resistance
Rusakov Dmitriy V., Kanash Elena V.
Plant, Soil and Environment. 2022 68(3). p.137
Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance
Sytar Oksana,
Brestic Marian,
Zivcak Marek,
Olsovska Katarina,
Kovar Marek,
Shao Hongbo, He Xiaolan
Science of The Total Environment. 2017 578 p.90
Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (2016)
Intelligent Robots and Drones for Precision Agriculture (2024)
Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
Hernanda Reza Adhitama Putra,
Lee Junghyun, Lee Hoonsoo
Applied Sciences. 2023 13(18). p.10420
Investigating crop performance on urban green roofs using hyperspectral data
Lee Hwang,
He Yuhong,
Isaac Marney E., Roberto Adriano
Ecological Informatics. 2024 81 p.102599
Digital Image Analysis Method for Estimation of Fusarium‐Damaged Kernels in Wheat
Maloney Peter V.,
Petersen Stine,
Navarro Rene A.,
Marshall David,
McKendry Anne L.,
Costa Jose M., Murphy J. Paul
Crop Science. 2014 54(5). p.2077
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform
Mohd Asaari Mohd Shahrimie,
Mishra Puneet,
Mertens Stien,
Dhondt Stijn,
Inzé Dirk,
Wuyts Nathalie, Scheunders Paul
ISPRS Journal of Photogrammetry and Remote Sensing. 2018 138 p.121
Pattern Recognition and Machine Intelligence (2017)
Fluorescence, PRI and canopy temperature for water stress detection in cereal crops
Panigada C.,
Rossini M.,
Meroni M.,
Cilia C.,
Busetto L.,
Amaducci S.,
Boschetti M.,
Cogliati S.,
Picchi V.,
Pinto F.,
Marchesi A., Colombo R.
International Journal of Applied Earth Observation and Geoinformation. 2014 30 p.167
Lights, camera, action: high-throughput plant phenotyping is ready for a close-up
Fahlgren Noah,
Gehan Malia A, Baxter Ivan
Current Opinion in Plant Biology. 2015 24 p.93
Leveraging Image Analysis for High-Throughput Plant Phenotyping
Das Choudhury Sruti,
Samal Ashok, Awada Tala
Frontiers in Plant Science. 2019 10
Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform
Asaari Mohd Shahrimie Mohd,
Mertens Stien,
Dhondt Stijn,
Inzé Dirk,
Wuyts Nathalie, Scheunders Paul
Computers and Electronics in Agriculture. 2019 162 p.749
A real-time phenotyping framework using machine learning for plant stress severity rating in soybean
Naik Hsiang Sing,
Zhang Jiaoping,
Lofquist Alec,
Assefa Teshale,
Sarkar Soumik,
Ackerman David,
Singh Arti,
Singh Asheesh K., Ganapathysubramanian Baskar
Plant Methods. 2017 13(1).
A Review of Imaging Techniques for Plant Phenotyping
Li Lei,
Zhang Qin, Huang Danfeng
Sensors. 2014 14(11). p.20078
Smart solution for leaf stress detection and classification a research pattern
Gautam Vinay, Rani Jyoti
Materials Today: Proceedings. 2022 60 p.1857
Development of a diurnal dehydration index for spring barley phenotyping
Rischbeck Pablo,
Baresel Peter,
Elsayed Salah,
Mistele Bodo, Schmidhalter Urs
Functional Plant Biology. 2014 41(12). p.1249
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Landcover classification with self-taught learning on archetypal dictionaries
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2015)
Intelligent Data Mining and Fusion Systems in Agriculture (2020)
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Franceschini Marston Héracles Domingues,
Bartholomeus Harm,
van Apeldoorn Dirk Frederik,
Suomalainen Juha, Kooistra Lammert
Remote Sensing. 2019 11(3). p.224
PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments
Valle Benoît,
Simonneau Thierry,
Boulord Romain,
Sourd Francis,
Frisson Thibault,
Ryckewaert Maxime,
Hamard Philippe,
Brichet Nicolas,
Dauzat Myriam, Christophe Angélique
Plant Methods. 2017 13(1).
Sustainable Agriculture in the Era of the OMICs Revolution (2023)
Comparison of two methods for indirect measurement of atmospheric dust deposition: Street-dust composition and vegetation-health status derived from hyperspectral image data
Žibret Gorazd, Kopačková Veronika
Ambio. 2019 48(4). p.423
Sparse representation-based archetypal graphs for spectral clustering
2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2017)
An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review
Anshori Muhammad Fuad,
Dirpan Andi,
Sitaresmi Trias,
Rossi Riccardo,
Farid Muh,
Hairmansis Aris,
Purwoko Bambang,
Suwarno Willy Bayuardi, Nugraha Yudhistira
Heliyon. 2023 9(11). p.e21650
Low-cost assessment of wheat resistance to yellow rust through conventional RGB images
Zhou B.,
Elazab A.,
Bort J.,
Vergara O.,
Serret M.D., Araus J.L.
Computers and Electronics in Agriculture. 2015 116 p.20
Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery
Hermanns Floris,
Pohl Felix,
Rebmann Corinna,
Schulz Gundula,
Werban Ulrike, Lausch Angela
Remote Sensing. 2021 13(10). p.1885
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
Deery David,
Jimenez-Berni Jose,
Jones Hamlyn,
Sirault Xavier, Furbank Robert
Agronomy. 2014 4(3). p.349
Discriminative archetypal self-taught learning for multispectral landcover classification
2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS) (2016)
Biochemical, Physiological and Molecular Avenues for Combating Abiotic Stress Tolerance in Plants (2018)
Computer Vision, Pattern Recognition, Image Processing, and Graphics (2018)
Hyperspectral imaging for rice cultivation: Applications, methods and challenges
Arias Fernando,
Zambrano Maytee,
Broce Kathia,
Medina Carlos,
Pacheco Hazel, Nunez Yerenis
AIMS Agriculture and Food. 2021 6(1). p.273
Deep learning techniques for hyperspectral image analysis in agriculture: A review
Guerri Mohamed Fadhlallah,
Distante Cosimo,
Spagnolo Paolo,
Bougourzi Fares, Taleb-Ahmed Abdelmalik
ISPRS Open Journal of Photogrammetry and Remote Sensing. 2024 12 p.100062
Breeding and Domesticating Crops Adapted to Drought and Salinity: A New Paradigm for Increasing Food Production
Fita Ana,
Rodríguez-Burruezo Adrián,
Boscaiu Monica,
Prohens Jaime, Vicente Oscar
Frontiers in Plant Science. 2015 6
Computational Sustainability (2016)
Molecular Mapping of Water-Stress Responsive Genomic Loci in Lettuce (Lactuca spp.) Using Kinetics Chlorophyll Fluorescence, Hyperspectral Imaging and Machine Learning
Kumar Pawan,
Eriksen Renee L.,
Simko Ivan, Mou Beiquan
Frontiers in Genetics. 2021 12
Phenocave: An Automated, Standalone, and Affordable Phenotyping System for Controlled Growth Conditions
Leiva Fernanda,
Vallenback Pernilla,
Ekblad Tobias,
Johansson Eva, Chawade Aakash
Plants. 2021 10(9). p.1817
Sorting biotic and abiotic stresses on wild rocket by leaf-image hyperspectral data mining with an artificial intelligence model
Navarro Alejandra,
Nicastro Nicola,
Costa Corrado,
Pentangelo Alfonso,
Cardarelli Mariateresa,
Ortenzi Luciano,
Pallottino Federico,
Cardi Teodoro, Pane Catello
Plant Methods. 2022 18(1).
Exploiting High-Throughput Indoor Phenotyping to Characterize the Founders of a Structured B. napus Breeding Population
Ebersbach Jana,
Khan Nazifa Azam,
McQuillan Ian,
Higgins Erin E.,
Horner Kyla,
Bandi Venkat,
Gutwin Carl,
Vail Sally Lynne,
Robinson Steve J., Parkin Isobel A. P.
Frontiers in Plant Science. 2022 12
A Review on Sensing Technologies for High-Throughput Plant Phenotyping
Ma Zhenyu,
Rayhana Rakiba,
Feng Ke,
Liu Zheng,
Xiao Gaozhi,
Ruan Yuefeng, Sangha Jatinder S.
IEEE Open Journal of Instrumentation and Measurement. 2022 1 p.1
Integration of multi-omics techniques and physiological phenotyping within a holistic phenomics approach to study senescence in model and crop plants
Großkinsky Dominik K,
Syaifullah Syahnada Jaya, Roitsch Thomas
Journal of Experimental Botany. 2018 69(4). p.825
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
Lowe Amy,
Harrison Nicola, French Andrew P
Plant Methods. 2017 13(1).
Assessing the Reliability of Thermal and Optical Imaging Techniques for Detecting Crop Water Status under Different Nitrogen Levels
Masseroni Daniele,
Ortuani Bianca,
Corti Martina,
Gallina Pietro Marino,
Cocetta Giacomo,
Ferrante Antonio, Facchi Arianna
Sustainability. 2017 9(9). p.1548
MFC-CNN: An automatic grading scheme for light stress levels of lettuce (Lactuca sativa L.) leaves
Hao Xia,
Jia Jingdun,
Gao Wanlin,
Guo Xuchao,
Zhang Wenxin,
Zheng Lihua, Wang Minjuan
Computers and Electronics in Agriculture. 2020 179 p.105847
Detection of Plant Responses to Drought using Close-Range Hyperspectral Imaging in a High-Throughput Phenotyping Platform
2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (2018)
Agronomic Crops (2019)
Genomics of Plant Genetic Resources (2014)
Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test
Junttila Samuli,
Kaasalainen Sanna,
Vastaranta Mikko,
Hakala Teemu,
Nevalainen Olli, Holopainen Markus
Remote Sensing. 2015 7(10). p.13863
Detection of early plant stress responses in hyperspectral images
Behmann Jan,
Steinrücken Jörg, Plümer Lutz
ISPRS Journal of Photogrammetry and Remote Sensing. 2014 93 p.98
Utilizing spatial variability from hyperspectral imaging to assess variation in maize seedlings
Tirado Sara B.,
Dennis Susan St.,
Enders Tara A., Springer Nathan M.
The Plant Phenome Journal. 2021 4(1).
A low-cost and open-source platform for automated imaging
Lien Max R.,
Barker Richard J.,
Ye Zhiwei,
Westphall Matthew H.,
Gao Ruohan,
Singh Aditya,
Gilroy Simon, Townsend Philip A.
Plant Methods. 2019 15(1).
A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat
Shabannejad Morteza,
Bihamta Mohammad-Reza,
Majidi-Hervan Eslam,
Alipour Hadi, Ebrahimi Asa
Plant Methods. 2020 16(1).
Sensors and imaging techniques for the assessment of the delay of wheat senescence induced by fungicides
Berdugo Carlos Andres,
Mahlein Anne-Katrin,
Steiner Ulrike,
Dehne Heinz-Wilhelm, Oerke Erich-Christian
Functional Plant Biology. 2013 40(7). p.677
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
Virlet Nicolas,
Sabermanesh Kasra,
Sadeghi-Tehran Pouria, Hawkesford Malcolm J.
Functional Plant Biology. 2017 44(1). p.143
Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability
Żelazny Wiktor R., Lukáš Jan
Remote Sensing. 2020 12(20). p.3462
Archetypal Networks
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (2015)
Breeding, Genetics, and Genomics Approaches for Improving Fusarium Wilt Resistance in Major Grain Legumes
Jha Uday Chand,
Bohra Abhishek,
Pandey Shailesh, Parida Swarup Kumar
Frontiers in Genetics. 2020 11
New Frontiers in Stress Management for Durable Agriculture (2020)
Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress
Mohd Asaari Mohd Shahrimie,
Mertens Stien,
Verbraeken Lennart,
Dhondt Stijn,
Inzé Dirk,
Bikram Koirala, Scheunders Paul
Computers and Electronics in Agriculture. 2022 195 p.106806
Machine Learning for High-Throughput Stress Phenotyping in Plants
Singh Arti,
Ganapathysubramanian Baskar,
Singh Asheesh Kumar, Sarkar Soumik
Trends in Plant Science. 2016 21(2). p.110
Proximal Hyperspectral Imaging Detects Diurnal and Drought-Induced Changes in Maize Physiology
Mertens Stien,
Verbraeken Lennart,
Sprenger Heike,
Demuynck Kirin,
Maleux Katrien,
Cannoot Bernard,
De Block Jolien,
Maere Steven,
Nelissen Hilde,
Bonaventure Gustavo,
Crafts-Brandner Steven J.,
Vogel Jonathan T.,
Bruce Wesley,
Inzé Dirk, Wuyts Nathalie
Frontiers in Plant Science. 2021 12
Spectral Reflectance of Palauan Reef-Building Coral with Different Symbionts in Response to Elevated Temperature
Russell Brandon,
Dierssen Heidi,
LaJeunesse Todd,
Hoadley Kenneth,
Warner Mark,
Kemp Dustin, Bateman Timothy
Remote Sensing. 2016 8(3). p.164
Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications
Cai Jinhai,
Okamoto Mamoru,
Atieno Judith,
Sutton Tim,
Li Yongle,
Miklavcic Stanley J., Kalaitzis Panagiotis
PLOS ONE. 2016 11(6). p.e0157102
Salt stress under the scalpel – dissecting the genetics of salt tolerance
Morton Mitchell J. L.,
Awlia Mariam,
Al‐Tamimi Nadia,
Saade Stephanie,
Pailles Yveline,
Negrão Sónia, Tester Mark
The Plant Journal. 2019 97(1). p.148
Hyperspectral remote sensing of grapevine drought stress
Zovko M.,
Žibrat U.,
Knapič M.,
Kovačić M. Bubalo, Romić D.
Precision Agriculture. 2019 20(2). p.335
Computer Vision and Pattern Recognition in Environmental Informatics (2016)
Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images
Wahabzada Mirwaes,
Mahlein Anne-Katrin,
Bauckhage Christian,
Steiner Ulrike,
Oerke Erich-Christian,
Kersting Kristian, Lightfoot David A
PLOS ONE. 2015 10(1). p.e0116902
Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements
Thomas Stefan,
Wahabzada Mirwaes,
Kuska Matheus Thomas,
Rascher Uwe, Mahlein Anne-Katrin
Functional Plant Biology. 2017 44(1). p.23
Phenomics: unlocking the hidden genetic variation for breaking the barriers in yield and stress tolerance
Kumar Sudhir,
Raju Dhandapani,
Sahoo Rabi N., Chinnusamy Viswanathan
Indian Journal of Plant Physiology. 2016 21(4). p.409
A generic workflow combining deep learning and chemometrics for processing close-range spectral images to detect drought stress in Arabidopsis thaliana to support digital phenotyping
Mishra Puneet,
Sadeh Roy,
Ryckewaert Maxime,
Bino Ehud,
Polder Gerrit,
Boer Martin P.,
Rutledge Douglas N., Herrmann Ittai
Chemometrics and Intelligent Laboratory Systems. 2021 216 p.104373
Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants
Hale Gabriella,
Yuan Ning,
Mendu Lavanya,
Ritchie Glen,
Mendu Venugopal, Deshmukh Rupesh Kailasrao
PLOS ONE. 2024 19(3). p.e0300667
Plant drought impact detection using ultra-high spatial resolution hyperspectral images and machine learning
Dao Phuong D.,
He Yuhong, Proctor Cameron
International Journal of Applied Earth Observation and Geoinformation. 2021 102 p.102364
LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics?
Lin Yi
Computers and Electronics in Agriculture. 2015 119 p.61
Spatio-temporal altimeter waveform retracking via sparse representation and conditional random fields
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2015)
Nutrient-Dense Orange-Fleshed Sweetpotato: Advances in Drought-Tolerance Breeding and Understanding of Management Practices for Sustainable Next-Generation Cropping Systems in Sub-Saharan Africa
Low Jan W.,
Ortiz Rodomiro,
Vandamme Elke,
Andrade Maria,
Biazin Birhanu, Grüneberg Wolfgang J.
Frontiers in Sustainable Food Systems. 2020 4
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
Arunachalam Ajay, Andreasson Henrik
Computers & Electrical Engineering. 2021 92 p.107098
New strategies on the application of artificial intelligence in the field of phytoremediation
Singh Pratyasha,
Pani Aparupa,
Mujumdar Arun S., Shirkole Shivanand S.
International Journal of Phytoremediation. 2023 25(4). p.505
Learned features of leaf phenotype to monitor maize water status in the fields
Zhuang Shuo,
Wang Ping,
Jiang Boran, Li Maosong
Computers and Electronics in Agriculture. 2020 172 p.105347
Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach
Pinto Francisco,
Mielewczik Michael,
Liebisch Frank,
Walter Achim,
Greven Hartmut,
Rascher Uwe, Jiang Bin
PLoS ONE. 2013 8(9). p.e73234
High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging
Pandey Piyush,
Ge Yufeng,
Stoerger Vincent, Schnable James C.
Frontiers in Plant Science. 2017 8
A Review on Analysis Method of Proximal Hyperspectral Imaging for Studying Plant Traits
Wen Lin Jian,
Mohd Asaari Mohd Shahrimie,
Ibrahim Haidi,
Khairi Ishak Mohamad, Din Abdul Sattar
Pertanika Journal of Science and Technology. 2023 31(6). p.2823
Phenomics in Crop Plants: Trends, Options and Limitations (2015)
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Sarkar Sayantan,
Ramsey A. Ford,
Cazenave Alexandre-Brice, Balota Maria
Frontiers in Plant Science. 2021 12
Phenotyping plants: genes, phenes and machines
Pieruschka Roland, Poorter Hendrik
Functional Plant Biology. 2012 39(11). p.813
A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping
Gill Taqdeer,
Gill Simranveer K.,
Saini Dinesh K.,
Chopra Yuvraj,
de Koff Jason P., Sandhu Karansher S.
Phenomics. 2022 2(3). p.156
Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants
Wahabzada Mirwaes,
Mahlein Anne-Katrin,
Bauckhage Christian,
Steiner Ulrike,
Oerke Erich-Christian, Kersting Kristian
Scientific Reports. 2016 6(1).
Unsupervised domain adaptation for early detection of drought stress in hyperspectral images
Schmitter P.,
Steinrücken J.,
Römer C.,
Ballvora A.,
Léon J.,
Rascher U., Plümer L.
ISPRS Journal of Photogrammetry and Remote Sensing. 2017 131 p.65
Rapid online plant leaf area change detection with high-throughput plant image data
Zhan Yinglun,
Zhang Ruizhi,
Zhou Yuzhen,
Stoerger Vincent,
Hiller Jeremy,
Awada Tala, Ge Yufeng
Journal of Applied Statistics. 2023 50(14). p.2984
Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art
Mahlein A.-K.,
Kuska M.T.,
Behmann J.,
Polder G., Walter A.
Annual Review of Phytopathology. 2018 56(1). p.535
Hyperspectral vegetation indices for predicting onion (Allium cepa L.) yield spatial variability
Marino S., Alvino A.
Computers and Electronics in Agriculture. 2015 116 p.109
Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high‐throughput image analysis
Neumann Kerstin,
Klukas Christian,
Friedel Swetlana,
Rischbeck Pablo,
Chen Dijun,
Entzian Alexander,
Stein Nils,
Graner Andreas, Kilian Benjamin
Plant, Cell & Environment. 2015 38(10). p.1980
Data Mining and Pattern Recognition in Agriculture
Bauckhage Christian, Kersting Kristian
KI - Künstliche Intelligenz. 2013 27(4). p.313
