LemnaTec is cited in hundreds of publications

Arora A., Misra T., Kumar M., Marwaha S., Kumar S., Chinnusamy V. (2023) Computer Vision Approaches for Plant Phenotypic Parameter Determination. In: S. Chaudhary, C. M. Biradar, S. Divakaran, M. S. Raval (Eds). Digital Ecosystem for Innovation in Agriculture. Studies in Big Data. Springer Nature Singapore, Singapore: 263–270. DOI:10.1007/978-981-99-0577-5_13

Bajsa-Hirschel J., Pan Z., Pandey P., Asolkar R.N., Chittiboyina A.G., Boddy L., Machingura M.C., Duke S.O. (2022) Spliceostatin C, a component of a microbial bioherbicide, is a potent phytotoxin that inhibits the spliceosome. Frontiers in Plant Science, 13, 1019938. DOI:10.3389/fpls.2022.1019938

Cardellicchio A., Solimani F., Dimauro G., Petrozza A., Summerer S., Cellini F., Renò V. (2023) Detection of tomato plant phenotyping traits using YOLOv5-based single stage detectors. Computers and Electronics in Agriculture, 207, 107757. DOI:10.1016/j.compag.2023.107757

Chen H., King R., Smith D., Bayon C., Ashfield T., Torriani S., Kanyuka K., Hammond-Kosack K., Bieri S., Rudd J. (2023) Combined pangenomics and transcriptomics reveals core and redundant virulence processes in a rapidly evolving fungal plant pathogen. BMC biology, 21, 24. DOI:10.1186/s12915-023-01520-6

Das Choudhury S., Saha S., Samal A., Mazis A., Awada T. (2023) Drought stress prediction and propagation using time series modeling on multimodal plant image sequences. Frontiers in Plant Science, 14, 1003150. DOI:10.3389/fpls.2023.1003150

Elangovan A., Duc N.T., Raju D., Kumar S., Singh B., Vishwakarma C., Gopala Krishnan S., Ellur R.K., Dalal M., Swain P., Dash S.K., Singh M.P., Sahoo R.N., Dinesh G.K., Gupta P., Chinnusamy V. (2023) Imaging Sensor-Based High-Throughput Measurement of Biomass Using Machine Learning Models in Rice. Agriculture, 13, 852. DOI:10.3390/agriculture13040852

Genangeli A., Avola G., Bindi M., Cantini C., Cellini F., Grillo S., Petrozza A., Riggi E., Ruggiero A., Summerer S., Tedeschi A., Gioli B. (2023) Low-Cost Hyperspectral Imaging to Detect Drought Stress in High-Throughput Phenotyping. Plants (Basel, Switzerland), 12. DOI:10.3390/plants12081730

Girija A., Canales F.J., Haddadi B.S., Dye R., Corke F., Han J., Brook J., Williams K., Beckmann M., Prats E., Doonan J.H., Mur L.A.J. (2023) Metabolomic approaches highlight two mechanisms of accelerated grain filling in Mediterranean oat (Avena sativa L.) cultivars during drought. DOI:10.1101/2023.06.28.546978

Jacobi J., Budahn H., Nothnagel T., König J. (2023) Studies on the Identification of Resistance to Fusarium oxysporum (Schlecht.) in Different Genetic Backgrounds of Asparagus officinalis (L.) and Its Defense Responses. Horticulturae, 9, 158. DOI:10.3390/horticulturae9020158

Karjalainen J., Hu X., Mäkinen M., Karjalainen A., Järvistö J., Järvenpää K., Sepponen M., Leppänen M.T. (2023) Sulfate sensitivity of aquatic organism in soft freshwaters explored by toxicity tests and species sensitivity distribution. Ecotoxicology and Environmental Safety, 258, 114984. DOI:10.1016/j.ecoenv.2023.114984

Kim J., Lee C., Park J.-E., Mansoor S., Chung Y.S., Kim K. (2023) Drought Stress Restoration Frequencies of Phenotypic Indicators in Early Vegetative Stages of Soybean (Glycine max L.). Sustainability, 15, 4852. DOI:10.3390/su15064852

Kim J., Lee C., Park J., Kim N., Kim S.-L., BAEK J., Chung Y.-S., Kim K. (2023) Comparison of Various Drought Resistance Traits in Soybean (Glycine max L.) Based on Image Analysis for Precision Agriculture. Plants (Basel, Switzerland), 12. DOI:10.3390/plants12122331

Michaud O., Krahmer J., Galbier F., Lagier M., Galvão V.C., Ince Y.Ç., Trevisan M., Knerova J., Dickinson P., Hibberd J.M., Zeeman S.C., Fankhauser C. (2023) Abscisic acid modulates neighbor proximity-induced leaf hyponasty in Arabidopsis. Plant Physiol., 191, 542–557. DOI:10.1093/plphys/kiac447

Ngo H.T.T., Cavagnaro T.R., Jewell N., Brien C.J., Berger B., Watts-Williams S.J. (2023) High-throughput shoot phenotyping reveals temporal growth responses to nitrogen and inorganic and organic phosphorus sources in tomato. AoB PLANTS. DOI:10.1093/aobpla/plad011

Pasam R.K., Kant S., Thoday-Kennedy E., Dimech A., Joshi S., Keeble-Gagnere G., Forrest K., Tibbits J., Hayden M. (2023) Haplotype-Based Genome-Wide Association Analysis Using Exome Capture Assay and Digital Phenotyping Identifies Genetic Loci Underlying Salt Tolerance Mechanisms in Wheat. Plants (Basel, Switzerland), 12. DOI:10.3390/plants12122367

Singh B., Kumar S., Elangovan A., Vasht D., Arya S., Duc N.T., Swami P., Pawar G.S., Raju D., Krishna H., Sathee L., Dalal M., Sahoo R.N., Chinnusamy V. (2023) Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. Frontiers in Plant Science, 14, 1214801. DOI:10.3389/fpls.2023.1214801

Tang Z., Chen Z., Gao Y., Xue R., Geng Z., Bu Q., Wang Y., Chen X., Jiang Y., Chen F., Yang W., Hu W. (2023) A Strategy for the Acquisition and Analysis of Image-Based Phenome in Rice during the Whole Growth Period. Plant phenomics (Washington, D.C.), 5, 58. DOI:10.34133/plantphenomics.0058

Thoday-Kennedy E., Dimech A.M., Joshi S., Daetwyler H.D., Hudson D., Spangenberg G., Hayden M., Kant S. (2023) An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping. Data in Brief, 46, 108787. DOI:10.1016/j.dib.2022.108787

Wang W., Guo W., Le L., Yu J., Wu Y., Li D., Wang Y., Wang H., Lu X., Qiao H., Gu X., Tian J., Zhang C., Pu L. (2023) Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize. Molecular Plant, 16, 354–373. DOI:10.1016/j.molp.2022.11.016

Williams K., Hepworth J., Nichols B.S., Corke F., Woolfenden H., Paajanen P., Steuernagel B., Østergaard L., Morris R.J., Doonan J.H., Wells R. (2023) Integrated Phenomics and Genomics reveals genetic loci associated with inflorescence growth in Brassica napus. DOI:10.1101/2023.03.31.535149

Xu Z., Wu C. (2023) Combination of Transfer Deep Learning and Classical Machine Learning Models for Multi-View Image Analysis. In: IOCMA 2023. MDPI, Basel Switzerland: 13. DOI:10.3390/IOCMA2023-14401

Zahn T., Zhu Z., Ritoff N., Krapf J., Junker A., Altmann T., Schmutzer T., Tüting C., Kastritis P.L., Babben S., Quint M., Pillen K., Maurer A. (2023) Novel exotic alleles of EARLY FLOWERING 3 determine plant development in barley. J. Exp. Bot. DOI:10.1093/jxb/erad127

Acosta-Gamboa L.M., Campbell Z.C., Gao F., Babst B., Lorence A. (2022) A Novel High-Throughput Phenotyping Hydroponic System for Nitrogen Deficiency Studies in Arabidopsis thaliana. Methods Mol Biol, 2539, 19–24. DOI:10.1007/978-1-0716-2537-8_3

Amitrano C., Junker A., D’Agostino N., Pascale S. de, Micco V. de (2022) Integration of high-throughput phenotyping with anatomical traits of leaves to help understanding lettuce acclimation to a changing environment. Planta, 256, 68. DOI: 10.1007/s00425-022-03984-2

Bannihatti R.K., Sinha P., Raju D., Das S., Mandal S.N., Raje R.S., Viswanathan C., Kumar S., Gaikwad K., Aggarwal R. (2022) Image Based High throughput Phenotyping for Fusarium Wilt Resistance in Pigeon Pea (Cajanus cajan). Phytoparasitica, 1–16. DOI:10.1007/s12600-022-00993-5

Cárdenas D.M., Bajsa-Hirschel J., Cantrell C.L., Rial C., Varela R.M., Molinillo J.M.G., Macías F.A. (2022) Evaluation of the phytotoxic and antifungal activity of C17 -sesquiterpenoids as potential biopesticides. Pest Manag Sci. DOI:10.1002/ps.7042

Chavez Mendoza K., Peña-Valdivia C.B., Hernández Rodríguez M., Vázquez Sánchez M., Morales Elías N.C., Jiménez Galindo J.C., García Esteva A., Padilla Chacón D. (2022) Phenotypic, Anatomical, and Diel Variation in Sugar Concentration Linked to Cell Wall Invertases in Common Bean Pod Racemes under Water Restriction. Plants (Basel, Switzerland), 11. DOI:10.3390/plants11131622

Chen L., Strauch M., Daub M., Luigs H.-G., Jansen M., Merhof D. (2022) Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2022, 2128–2131. DOI: 10.1109/EMBC48229.2022.9871517

Cieslak M., Khan N., Ferraro P., Soolanayakanahally R., Robinson S.J., Parkin I., McQuillan I., Prusinkiewicz P. (2022) L-system models for image-based phenomics: case studies of maize and canola. In silico Plants, 4. DOI:10.1093/insilicoplants/diab039

Clauw P., Kerdaffrec E., Gunis J., Reichardt-Gomez I., Nizhynska V., Koemeda S., Jez J., Nordborg M. (2022) Locally adaptive temperature response of vegetative growth in Arabidopsis thaliana. Elife, 11. DOI: 10.7554/eLife.77913

Danzi D., Paola D. de, Petrozza A., Summerer S., Cellini F., Pignone D., Janni M. (2022) The Use of Near-Infrared Imaging (NIR) as a Fast Non-Destructive Screening Tool to Identify Drought-Tolerant Wheat Genotypes. Agriculture, 12, 537. DOI:10.3390/agriculture12040537

Deblieck M., Szilagyi G., Andrii F., Saranga Y., Lauterberg M., Neumann K., Krugman T., Perovic D., Pillen K., Ordon F. (2022) Dissection of a grain yield QTL from wild emmer wheat reveals sub-intervals associated with culm length and kernel number. Frontiers in genetics, 13, 955295. DOI:10.3389/fgene.2022.955295

Dunlevy J.D., Blackmore D.H., Betts A., Jewell N., Brien C., Berger B., Walker R.R., Edwards E.J., Walker A.R. (2022) Investigating the effects of elevated temperature on salinity tolerance traits in grapevine rootstocks using high‐throughput phenotyping. Aust J Grape and Wine Res, 28, 276–291. DOI:10.1111/ajgw.12549

Gachoki P., Muraya M., Njoroge G. (2022) Features Selection in Statistical Classification of High Dimensional Image Derived Maize (Zea Mays L.) Phenomic Data. AJAMS, 10, 44–51. DOI: 10.12691/ajams-10-2-2

Khapte P.S., Kumar P., Wakchaure G.C., Jangid K.K., Colla G., Cardarelli M., Rane J. (2022) Application of Phenomics to Elucidate the Influence of Rootstocks on Drought Response of Tomato. Agronomy, 12, 1529. DOI:10.3390/agronomy12071529

Koh J.C.O., Banerjee B.P., Spangenberg G., Kant S. (2022) Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping. New Phytol, 233, 2659–2670. DOI: 10.1111/nph.17947

Laxman R.H., Hemamalini P., Namratha M.R., Bhatt R.M., Sadashiva A.T. (2022) Phenotyping Deficit Moisture Stress Tolerance in Tomato Using Image Derived Digital Features. IJBSM, 13, 339–347. DOI:10.23910/1.2022.2544

Medina-Jimenez K., Arteaga-Vazquez M.A., Lorence A. (2022) An Automated High-Throughput Phenotyping System for Marchantia polymorpha. Methods Mol Biol, 2539, 11–17. DOI:10.1007/978-1-0716-2537-8_2

Min L.-J., Wang H., Bajsa-Hirschel J., Yu C.-S., Wang B., Yao M.-M., Han L., Cantrell C.L., Duke S.O., Sun N.-B., Liu X.-H. (2022) Novel Dioxolane Ring Compounds for the Management of Phytopathogen Diseases as Ergosterol Biosynthesis Inhibitors: Synthesis, Biological Activities, and Molecular Docking. J Agric Food Chem, 70, 4303–4315. DOI: 10.1021/acs.jafc.2c00541

Pabuayon I.C.M., Pabuayon I.L.B., Singh R.K., Ritchie G.L., los Reyes B.G. de (2022) Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta. PLoS ONE, 17, e0270931. DOI: 10.1371/journal.pone.0270931

Padilla-Chacón D., Peña-Valdivia C.B. (2022) High-Throughput Screening to Examine the Dynamic of Stay-Green by an Imaging System. Methods Mol Biol, 2539, 3–9. DOI:10.1007/978-1-0716-2537-8_1

Priya H., Dhar D.W., Singh R., Kumar S., Dhandapani R., Pandey R., Govindasamy V., Kumar A. (2022) Co-cultivation Approach to Decipher the Influence of Nitrogen-Fixing Cyanobacterium on Growth and N Uptake in Rice Crop. Current Microbiology, 79, 53. DOI:10.1007/s00284-021-02732-1

Qi M., Berry J.C., Veley K.W., O’Connor L., Finkel O.M., Salas-González I., Kuhs M., Jupe J., Holcomb E., Del Glavina Rio T., Creech C., Liu P., Tringe S.G., Dangl J.L., Schachtman D.P., Bart R.S. (2022) Identification of beneficial and detrimental bacteria impacting sorghum responses to drought using multi-scale and multi-system microbiome comparisons. ISME J, 1–13. DOI:10.1038/s41396-022-01245-4

Rossi R., Costafreda-Aumedes S., Summerer S., Moriondo M., Leolini L., Cellini F., Bindi M., Petrozza A. (2022) A Comparison of High-Throughput Imaging Methods for Quantifying Plant Growth Traits and Estimating Above-Ground Biomass Accumulation. SSRN Journal. DOI:10.2139/ssrn.4111955

Vishal M.K., Saluja R., Aggrawal D., Banerjee B., Raju D., Kumar S., Chinnusamy V., Sahoo R.N., Adinarayana J. (2022) Leaf Count Aided Novel Framework for Rice (Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications. Plants (Basel, Switzerland), 11. DOI: 10.3390/plants11192663

Xie Y., Plett D., Clarke K., Evans M., Garrard T., Butt M., Liu H. (2022) Application of Hyperspectral Imaging Technologies for Early Detection of Crown Rot Disease in Wheat Under Controlled Environment. SSRN Journal. DOI: 10.2139/ssrn.4217308

Wlodkowic D., Jansen M. (2022) High-throughput screening paradigms in ecotoxicity testing: Emerging prospects and ongoing challenges. Chemosphere, 307, 135929. DOI:10.1016/j.chemosphere.2022.135929

Zahn T., Zhu Z., Ritoff N., Krapf J., Junker A., Altmann T., Schmutzer T., Tüting C., Kastritis P.L., Babben S., Quint M., Pillen K., Maurer A. (2022) Exotic alleles of EARLY FLOWERING 3 determine plant development and grain yield in barley. DOI: 10.1101/2022.07.15.500212

Zhang H., Ge Y., Xie X., Atefi A., Wijewardane N.K., Thapa S. (2022) High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion. Plant Methods, 18, 60. DOI: 10.1186/s13007-022-00892-0

Acosta-Gamboa Lucia M., Nirman N., Karina M.-J., Campbell Zachary C., Cunningham Shannon S., Ae L.J., Argelia L. (2021) Myo -inositol Oxygenase Overexpression Rescues Vitamin C Deficient Arabidopsis (vtc ) Mutants.

Avramidou E.V., Moysiadis T., Ganopoulos I., Michailidis M., Kissoudis C., Valasiadis D., Kazantzis K., Tsaroucha E., Tsaftaris A., Molassiotis A., Aravanopoulos F.A., Xanthopoulou A. (2021) Phenotypic, Genetic, and Epigenetic Variation among Diverse Sweet Cherry Gene Pools. Agronomy, 11, 680.

Bashyam S., Choudhury S.D., Samal A., Awada T. (2021) Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis. Remote Sensing, 13, 961.

Bacher H., Zhu F., Gao T., Liu K., Dhatt B.K., Awada T., Zhang C., Distelfeld A., Yu H., Peleg Z., Walia H. (2021) Wild emmer introgression alters root-to-shoot growth dynamics in durum wheat in response to water stress. Plant Physiol., 187, 1149–1162. DOI:10.1093/plphys/kiab292

Borjigin C., Schilling R.K., Jewell N., Brien C., Sanchez-Ferrero J.C., Eckermann P.J., Watson-Haigh N.S., Berger B., Pearson A.S., Roy S.J. (2021) Identifying the genetic control of salinity tolerance in the bread wheat landrace Mocho de Espiga Branca. Functional plant biology FPB, 48, 1148–1160.

Camargo Rodriguez A.V. (2021) Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat. Genes, 12.

Cayetano-Marcial M.I., Peña-Valdivia C.B., Esteva A.G., Galindo J.J., Escobedo I.G., Chacón D.P. (2021)  Humidity Restriction, High Night Temperature and their Combination, during Post Flowering on Common Bean (Phaseolus vulgaris L.) Canopy and Pod Senescence. LR.

Chai Y.N., Ge Y., Stoerger V., Schachtman D.P. (2021) High-resolution phenotyping of sorghum genotypic and phenotypic responses to low nitrogen and synthetic microbial communities. Plant Cell Environ.

Chen L., Daub M., Luigs H.-G., Jansen M., Strauch M., Merhof D. High-throughput Phenotyping of Nematode Cysts.

Cieslak M., Khan N., Ferraro P., Soolanayakanahally R., Robinson S.J., Parkin I., McQuillan I., Prusinkiewicz P. (2021) L-system models for image-based phenomics: case studies of maize and canola. In silico Plants.

Dar Z.A., Dar S.A., Khan J.A., Lone A.A., Langyan S., Lone B.A., Kanth R.H., Iqbal A., Rane J., Wani S.H., Alfarraj S., Alharbi S.A., Brestic M., Ansari M.J. (2021) Identification for surrogate drought tolerance in maize inbred lines utilizing high-throughput phenomics approach. PLoS ONE, 16, e0254318.

Dissanayake R., Cogan N.O.I., Smith K.F., Kaur S. (2021) Application of Genomics to Understand Salt Tolerance in Lentil. Genes, 12, 332.

Dwivedi P., Ramawat N., Raju D., Dhawan G., Gopala Krishnan S., Chinnusamy V., Bhowmick P.K., Vinod K.K., Pal M., Nagarajan M., Ellur R.K., Bollinedi H., Singh A.K. (2021) Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping. Frontiers in Plant Science, 12, 752730. DOI: 10.3389/fpls.2021.752730

Dodig D., Božinović S., Nikolić A., Zorić M., Vančetović J., Ignjatović-Micić D., Delić N., Weigelt-Fischer K., Altmann T., Junker A. (2021) Dynamics of Maize Vegetative Growth and Drought Adaptability Using Image-Based Phenotyping Under Controlled Conditions. Frontiers in Plant Science, 12, 652116.

Fadoul H.E., Martínez Rivas F.J., Neumann K., Balazadeh S., Fernie A.R., Alseekh S. (2021) Comparative Molecular and Metabolic Profiling of Two Contrasting Wheat Cultivars under Drought Stress. International Journal of Molecular Sciences, 22.

Faralli M., Williams K., Corke F., Li M., Doonan J.H., Varotto C. (2021) Interspecific and intraspecific phenotypic diversity for drought adaptation in bioenergy Arundo species. GCB Bioenergy, 13, 753–769.

Ghahremani M., Williams K., Corke F.M.K., Tiddeman B., Liu Y., Doonan J.H. (2021) Deep Segmentation of Point Clouds of Wheat. Frontiers in Plant Science, 12, 608732.

Ghahremani M., Williams K., Corke F., Tiddeman B., Liu Y., Wang X., Doonan J.H. (2021) Direct and accurate feature extraction from 3D point clouds of plants using RANSAC. Computers and Electronics in Agriculture, 187, 106240.

Henke M., Neumann K., Altmann T., Gladilin E. (2021) Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg). Agriculture, 11, 1098.

Herritt M.T., Long J.C., Roybal M.D., Moller D.C., Mockler T.C., Pauli D., Thompson A.L. (2021) FLIP: FLuorescence Imaging Pipeline for field-based chlorophyll fluorescence images. SoftwareX, 14, 100685.

Joshi S., Thoday-Kennedy E., Daetwyler H.D., Hayden M., Spangenberg G., Kant S. (2021) High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance. PLoS ONE, 16, e0254908.

LeBauer D., Burnette M., Fahlgren N., Kooper R., McHenry K., Stylianou A. (2021) What Does TERRA-REF’s High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community? In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). IEEE.

Miao C., Guo A., Thompson A.M., Yang J., Ge Y., Schnable J.C. (2021) Automation of leaf counting in maize and sorghum using deep learning. Plant phenome j.

Misra T., Arora A., Marwaha S., Jha R.R., Ray M., Varghese E., Kumar S., Nigam A., Sahoo R.N., Chinnusamy V. (2021) Web-SpikeSegNet: Deep Learning Framework for Recognition and Counting of Spikes from Visual Images of Wheat Plants. Research Square.

Otero-Blanca A., Pérez-Llano Y., Reboledo-Blanco G., Lira-Ruan V., Padilla-Chacon D., Folch-Mallol J.L., Del Sánchez-Carbente M.R., Ponce De León I., Batista-García R.A. (2021) Physcomitrium patens Infection by Colletotrichum gloeosporioides: Understanding the Fungal-Bryophyte Interaction by Microscopy, Phenomics and RNA Sequencing. Journal of fungi (Basel, Switzerland), 7, 677.

Pabuayon I.C.M., Kitazumi A., Cushman K.R., Singh R.K., Gregorio G.B., Dhatt B., Zabet-Moghaddam M., Walia H., los Reyes B.G. de (2021) Novel and Transgressive Salinity Tolerance in Recombinant Inbred Lines of Rice Created by Physiological Coupling-Uncoupling and Network Rewiring Effects. Frontiers in Plant Science, 12, 615277.

Piccinini L., Cazzaniga S., Iacopino S., Ballottari M., Giuntoli B., Licausi F. (2021) A synthetic switch based on orange carotenoid protein to control blue light responses in chloroplasts: 26 pp.

Qi M., Berry J.C., Veley K., O’Connor L., Finkel O.M., Salas-González I., Kuhs M., Jupe J., Holcomb E., del Rio T.G., Creech C., Liu P., Tringe S., Dangl J.L., Schachtman D., Bart R.S. (2021) Identification of beneficial and detrimental bacteria that impact sorghum responses to drought using multi-scale and multi-system microbiome comparisons.

Rane J., Raina S.K., Govindasamy V., Bindumadhava H., Hanjagi P., Giri R., Jangid K.K., Kumar M., Nair R.M. (2021) Use of Phenomics for Differentiation of Mungbean (Vigna radiata L. Wilczek) Genotypes Varying in Growth Rates Per Unit of Water. Frontiers in Plant Science, 12, 692564.

Roireau J.H., Rosano R.J., Lazzara N.C., Chen T., Bajsa-Hirschel J., Schrader K.K., Duke S.O., Wykoff D., Giuliano R.M. (2020) Synthesis of Pyranopyrans Related to Diplopyrone and Evaluation as Antibacterials and Herbicides. Journal of Agricultural and Food Chemistry, 68, 9906–9916.

Sadeghi-Tehran P., Virlet N., Hawkesford M.J. (2021) A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery. Remote Sensing, 13, 898.

Saluja M., Zhu F., Yu H., Walia H., Sattler S.E. (2021) Loss of COMT activity reduces lateral root formation and alters the response to water limitation in sorghum brown midrib (bmr) 12 mutant. New Phytol, 229, 2780–2794.

Thoday-Kennedy E., Joshi S., Daetwyler H.D., Hayden M., Hudson D., Spangenberg G., Kant S. (2021) Digital Phenotyping to Delineate Salinity Response in Safflower Genotypes. Frontiers in Plant Science, 12, 662498.

Tran B.T.T., Cavagnaro T.R., Jewell N., Brien C., Berger B., Watts‐Williams S.J. (2021) High‐throughput phenotyping reveals growth of Medicago truncatula is positively affected by arbuscular mycorrhizal fungi even at high soil phosphorus availability. Plants People Planet, 3, 600–613.

Watts‐Williams S.J., Gill A.R., Jewell N., Brien C.J., Berger B., Tran B.T.T., Mace E., Cruickshank A.W., Jordan D.R., Garnett T., Cavagnaro T.R. (2021) Enhancement of sorghum grain yield and nutrition: A role for arbuscular mycorrhizal fungi regardless of soil phosphorus availability. Plants People Planet.

Zhu F., Saluja M., Dharni J.S., Paul P., Sattler S.E., Staswick P., Walia H., Yu H. (2021) PhenoImage An open‐source graphical user interface for plant image analysis. Plant phenome j., 4.

Zhou S., Chai X., Yang Z., Wang H., Yang C., Sun T. (2021) Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping. Plant Methods, 17, 48.

Acosta-Gamboa LM, Suxing L, Jarrod W C et al., 2020. Characterization of the response to abiotic stresses of high ascorbate Arabidopsis lines using phenomic approaches. Plant physiology and biochemistry PPB / Societe francaise de physiologie vegetale 151, 500–15. doi: 10.1016/j.plaphy.2020.03.038.

Ball K.R., Power S.A., Brien C., Woodin S., Jewell N., Berger B., Pendall E. (2020) High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture. PLoS ONE, 15, e0239673. DOI:10.1371/journal.pone.0239673

Banerjee B.P., Joshi S., Thoday-Kennedy E., Pasam R.K., Tibbits J., Hayden M., Spangenberg G., Kant S. (2020) High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response. J. Exp. Bot., 71, 4604–4615. DOI:10.1093/jxb/eraa143

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    Image-based estimation of oat panicle development using local texture patterns. In: Functional Plant Biology, S. 433. DOI: 10.1071/FP14056.

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Poiré, Richard; Chochois, Vincent; Sirault, Xavier R. R.; Vogel, John P.; Watt, Michelle; Furbank, Robert T. (2014) Digital imaging approaches for phenotyping whole plant nitrogen and phosphorus response in Brachypodium distachyon: Digital imaging approaches for phenotyping. In: Journal of Integrative Plant Biology, S. 781–796. DOI: 10.1111/jipb.12198.

Silva, Ferdinando M. L.; Donega, Mateus A.; Cerdeira, Antonio L.; Corniani, Natália; Velini, Edivaldo D.; Cantrell, Charles L.; Dayan, Franck E.; Coelho, Mariana N.; Shea, Katriona; Duke, Stephen O. (2014) Roots of the Invasive Species Carduus nutans L. and C. acanthoides L. Produce Large Amounts of Aplotaxene, a Possible Allelochemical. In: J Chem Ecol (Journal of Chemical Ecology), S. 276–284. DOI: 10.1007/s10886-014-0390-8.

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Saran, Raj K.; Ziegler, Melissa; Kudlie, Sara; Harrison, Danielle; Leva, David M.; Scherer, Clay; Coffelt, Mark A. (2014) Behavioral Effects and Tunneling Responses of Eastern Subterranean Termites (Isoptera: Rhinotermitidae) Exposed to Chlorantraniliprole-Treated Soils. In: Journal of Economic Entomology, S. 1878–1889. DOI: 10.1603/EC11393.

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    Expression of α-DIOXYGENASE 1 in tomato and Arabidopsis contributes to plant defenses against aphids. In: Molecular plant-microbe interactions : MPMI, S. 977–986. DOI: 10.1094/MPMI-01-13-0031-R..

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    A novel proteinase, SNOWY COTYLEDON4, is required for photosynthetic acclimation to higher light intensities in Arabidopsis. In: PLANT PHYSIOLOGY, S. 732–745. DOI: 10.1104/pp.113.216036.

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    Germanium as a tool to dissect boron toxicity effects in barley and wheat. In: Functional Plant Biology, S. 618. DOI: 10.1071/FP12329.

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    Aquatic toxicity of four veterinary drugs commonly applied in fish farming and animal husbandry. In: Chemosphere, S. 1253–1259. DOI: 10.1016/j.chemosphere.2013.04.057.

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    Ecotoxicological evaluation of wastewater in a municipal WWTP in Lisbon area (Portugal). In: Desalination and Water Treatment, S. 4162–4170. DOI: 10.1080/19443994.2013.768021.

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    Nuclear microscopy as a tool in TiO2 nanoparticles bioaccumulation studies in aquatic species. In: Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, S. 117–120. DOI: 10.1016/j.nimb.2012.12.049.

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    Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis – a novel phenotyping approach using laser scanning. In: Functional Plant Biology, S. 860. DOI: 10.1071/FP12018.

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