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Drought and heat stress tolerance screening in wheat using computed tomography

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Pteridophytes: evolutionary boon as medicinal plants

Goswami, H. K., Sen, K., & Mukhopadhyay, R. (2016) · Plant Genetic Resources · doi:10.1017/s1479262116000290 · Cited by 76 · see citing works

Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data

Chen, T., Yang, W., Zhang, H., Zhu, B., Zeng, R., Wang, X., Wang, S., Wang, L., Qi, H., Lan, Y., et al. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105708 · Cited by 76 · see citing works

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale

Paulus, S., & Mahlein, A. (2020) · GigaScience · doi:10.1093/gigascience/giaa090 · Cited by 75 · see citing works

Detection of peanut leaf spots disease using canopy hyperspectral reflectance

Chen, T., Zhang, J., Chen, Y., Wan, S., & Zhang, L. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.12.036 · Cited by 70 · see citing works

Morphological and physiological components mediating the silicon-induced enhancement of geranium essential oil yield under saline conditions

Hassanvand, F., Nejad, A. R., & Fanourakis, D. (2019) · Industrial Crops and Products · doi:10.1016/j.indcrop.2019.03.049 · Cited by 63 · see citing works

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

Zhao, L., Walkowiak, S., & Fernando, W. G. D. (2023) · Plants · doi:10.3390/plants12091852 · Cited by 59 · see citing works

Formation of polarity convergences underlying shoot outgrowths

Abley, K., Sauret-Gueto, S., Marée, A. F. M., & Coen, E. (2016) · eLife · doi:10.7554/elife.18165 · Cited by 58 · see citing works

Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance

Leucker, M., Wahabzada, M., Kersting, K., Peter, M., Beyer, W., Steiner, U., Mahlein, A., & Oerke, E.-C. (2016) · Functional Plant Biology · doi:10.1071/fp16121 · Cited by 54 · see citing works

Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

Sarkar, S., Ramsey, A. F., Cazenave, A.-B., & Balota, M. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.658621 · Cited by 52 · see citing works

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Geldhof, B., Pattyn, J., Eyland, D., Carpentier, S., & de Poel, B. V. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab407 · Cited by 52 · see citing works

Estimation of Nitrogen in Rice Crops from UAV-Captured Images

Colorado, J. D., Cera-Bornacelli, N., Caldas, J. S., Petró, E., Rebolledo, M. C., Cuellar, D., Calderón, F., Mondragon, I. F., & Jaramillo-Botero, A. (2020) · Remote Sensing · doi:10.3390/rs12203396 · Cited by 51 · see citing works

Analysis of the effect of chloroplast arrangement on optical properties of green tobacco leaves

Baránková, B., Lazár, D., & Nauš, J. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.011 · Cited by 51 · see citing works

Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yield‐limiting factors in wheat

Bruning, B., Berger, B., Lewis, M. M., Liu, H., & Garnett, T. (2020) · The Plant Phenome Journal · doi:10.1002/ppj2.20007 · Cited by 49 · see citing works

Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize

Loladze, A., Rodrigues, F. A., Toledo, F. H. R. B., Vicente, F. S., Gerard, B. G., & Prasanna, B. M. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00552 · Cited by 48 · see citing works

Characterizing damage evolution of CF/PEKK composites under tensile loading through multi-instrument structural health monitoring techniques

Yıldırım, C., Tabrizi, I. E., Al‐Nadhari, A., Topal, S., Beylergi̇l, B., & Yıldız, M. (2023) · Composites Part A Applied Science and Manufacturing · doi:10.1016/j.compositesa.2023.107817 · Cited by 47 · see citing works

Lettuce growth, morphology and critical leaf trait responses to far-red light during cultivation are low fluence and obey the reciprocity law

Zou, J., Fanourakis, D., Tsaniklidis, G., Cheng, R., Yang, Q., & Li, T. (2021) · Scientia Horticulturae · doi:10.1016/j.scienta.2021.110455 · Cited by 47 · see citing works

3D analysis of mitosis distribution highlights the longitudinal zonation and diarch symmetry in proliferation activity of the Arabidopsis thaliana root meristem

Lavrekha, V. V., Pasternak, T. P., Ivanov, V. B., Palme, K. J., & Mironova, V. (2017) · The Plant Journal · doi:10.1111/tpj.13720 · Cited by 46 · see citing works

Blue Light Improves Photosynthetic Performance during Healing and Acclimatization of Grafted Watermelon Seedlings

Moosavi-Nezhad, M., Salehi, R., Aliniaeifard, S., Tsaniklidis, G., Woltering, E. J., Fanourakis, D., Żuk‐Gołaszewska, K., & Kalaji, H. M. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22158043 · Cited by 46 · see citing works

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants

Caiming, G., Zafar, S., Fatima, Hasnain, Z., Aslam, N., Iqbal, N., Abbas, S., Li, H., Li, J., Chen, B., et al. (2024) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2901020 · Cited by 44 · see citing works

Postharvest application of single, multi-walled carbon nanotubes and nanographene oxide improves rose keeping quality

Ahmadi-Majd, M., Nejad, A. R., Mousavi-Fard, S., & Fanourakis, D. (2021) · The Journal of Horticultural Science and Biotechnology · doi:10.1080/14620316.2021.1993755 · Cited by 44 · see citing works

Flexible Self‐Powered Organic Photodetector with High Detectivity for Continuous On‐Plant Sensing

Schrickx, H. M., Gyurek, S., Moore, C., Hernández‐Pagán, E., Doherty, C. J., Kudenov, M. W., & O’Connor, B. T. (2024) · Advanced Optical Materials · doi:10.1002/adom.202400005 · Cited by 43 · see citing works

Engineering plants for tomorrow: how high-throughput phenotyping is contributing to the development of better crops

Campbell, Z. C., Acosta‐Gamboa, L., Nepal, N., & Lorence, A. (2018) · Phytochemistry Reviews · doi:10.1007/s11101-018-9585-x · Cited by 43 · see citing works

An improved method for the segmentation of roots from X-ray computed tomography 3D images: Rootine v.2

Phalempin, M., Lippold, E., Vetterlein, D., & Schlüter, S. (2021) · Plant Methods · doi:10.1186/s13007-021-00735-4 · Cited by 42 · see citing works

Evolutionary systems biology reveals patterns of rice adaptation to drought-prone agro-ecosystems

Groen, S. C., Joly‐Lopez, Z., Platts, A. E., Natividad, M. A., Fresquez, Z. A., Mauck, W. M., Quintana, M. R., Cabral, C. L. U., Torres, R. O., Satija, R., et al. (2021) · The Plant Cell · doi:10.1093/plcell/koab275 · Cited by 42 · see citing works

Monitoring the dynamics of wheat stem elongation: genotypes differ at critical stages

Kronenberg, L., Yu, K., Walter, A., & Hund, A. (2017) · Euphytica · doi:10.1007/s10681-017-1940-2 · Cited by 41 · see citing works

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Williams, D., Britten, A., McCallum, S., Jones, H. G., Aitkenhead, M. J., Karley, A. J., Loades, K., Prashar, A., & Graham, J. (2017) · Plant Methods · doi:10.1186/s13007-017-0226-y · Cited by 41 · see citing works

Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications

Cai, J., Okamoto, M., Atieno, J., Sutton, T., Li, Y., & Miklavcic, S. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157102 · Cited by 40 · see citing works

High nitrate supply promotes nitrate assimilation and alleviates photosynthetic acclimation of cucumber plants under elevated CO2

Dong, J., Li, X., Chu, W., & Duan, Z. (2017) · Scientia Horticulturae · doi:10.1016/j.scienta.2016.11.026 · Cited by 40 · see citing works

Phenotyping Flowering in Canola (Brassica napus L.) and Estimating Seed Yield Using an Unmanned Aerial Vehicle-Based Imagery

Zhang, T., Vail, S. L., Duddu, H., Parkin, I. A. P., Guo, X., Johnson, E. N., & Shirtliffe, S. J. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.686332 · Cited by 37 · see citing works

Improving Nitrogen Acquisition and Utilization Through Root Architecture Remodelling: Insight from Legumes

Nadeem, M., Yahya, M., Tong, J., Shah, L., Khan, S. U., Ali, A., Sher, A., Ullah, N., & Waheed, A. (2023) · Journal of Plant Growth Regulation · doi:10.1007/s00344-023-10938-9 · Cited by 34 · see citing works

Drought and heat stress: insights into tolerance mechanisms and breeding strategies for pigeonpea improvement

Bakala, H. S., Devi, J., Singh, G., & Singh, I. (2024) · Planta · doi:10.1007/s00425-024-04401-6 · Cited by 34 · see citing works

Contact Reflectance Spectroscopy for Rapid, Accurate, and Nondestructive Phytophthora infestans Clonal Lineage Discrimination

Gold, K. M., Townsend, P. A., Larson, E. R., Herrmann, I., & Gevens, A. J. (2019) · Phytopathology · doi:10.1094/phyto-08-19-0294-r · Cited by 34 · see citing works

QTL detection and putative candidate gene prediction for leaf rolling under moisture stress condition in wheat

Verma, A., Niranjana, M., Jha, S., Mallick, N., Agarwal, P., & Vinod, V. (2020) · Scientific Reports · doi:10.1038/s41598-020-75703-4 · Cited by 34 · see citing works

Root and canopy traits and adaptability genes explain drought tolerance responses in winter wheat

Nehe, A., Foulkes, J. M., Öztürk, Ĭ., Rasheed, A., York, L. M., Kefauver, S. C., Özdemir, F., & Morgounov, A. I. (2021) · PLoS ONE · doi:10.1371/journal.pone.0242472 · Cited by 34 · see citing works

Comparison of visible imaging, thermography and spectrometry methods to evaluate the effect of Heterodera schachtii inoculation on sugar beets

Joalland, S., Screpanti, C., Liebisch, F., Varella, H., Gaume, A., & Walter, A. (2017) · Plant Methods · doi:10.1186/s13007-017-0223-1 · Cited by 34 · see citing works

Light scattering in stacked mesophyll cells results in similarity characteristic of solar spectral reflectance and transmittance of natural leaves

Xu, K., & Ye, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31718-1 · Cited by 32 · see citing works

Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system

Yu, L., Sussman, H., Khmelnitsky, O., Ishka, M. R., Srinivasan, A., Nelson, A. D. L., & Julkowska, M. (2024) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiae237 · Cited by 31 · see citing works

Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan

Aski, M. S., Rai, N., Reddy, V. R. P., Gayacharan, C., Dikshit, H. K., MISHRA, G. P., Singh, D., Kumar, A., Pandey, R., Singh, M. P., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247810 · Cited by 31 · see citing works

Monitoring drought response and chlorophyll content in Quercus by consumer-grade, near-infrared (NIR) camera: a comparison with reflectance spectroscopy

Raddi, S., Giannetti, F., Martini, S., Farinella, F., Chirici, G., Tani, A., Maltoni, A., & Mariotti, B. (2021) · New Forests · doi:10.1007/s11056-021-09848-z · Cited by 31 · see citing works

Growth, photosynthetic function, and stomatal characteristics of Persian walnut explants in vitro under different light spectra

Saeedi, S. A., Vahdati, K., Sarikhani, S., Daylami, S. D., Davarzani, M., Gruda, N. S., & Aliniaeifard, S. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1292045 · Cited by 30 · see citing works

Plant trait measurement in 3D for growth monitoring

Paturkar, A., Gupta, G. S., & Bailey, D. G. (2022) · Plant Methods · doi:10.1186/s13007-022-00889-9 · Cited by 30 · see citing works

Can plant hormonomics be built on simple analysis? A review

Vrobel, O., & Tarkowski, P. (2023) · Plant Methods · doi:10.1186/s13007-023-01090-2 · Cited by 29 · see citing works

Analysis of root growth from a phenotyping data set using a density-based model

Kalogiros, D. I., Adu, M. O., White, P. J., Broadley, M. R., Draye, X., Mariya Ptashnyk, B., Bengough, A. G., & Dupuy, L. X. (2016) · Journal of Experimental Botany · doi:10.1093/jxb/erv573 · Cited by 29 · see citing works

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline

Dupuy, L. X., Wright, G., Thompson, J. A., Taylor, A., Dekeyser, S., White, C., Thomas, W. T. B., Nightingale, M., Hammond, J. P., Graham, N. S., et al. (2017) · Plant Methods · doi:10.1186/s13007-017-0207-1 · Cited by 28 · see citing works

Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants

Rajurkar, A. B., McCoy, S. M., Ruhter, J., Mulcrone, J., Freyfogle, L., & Leakey, A. D. B. (2022) · Plant Methods · doi:10.1186/s13007-022-00874-2 · Cited by 28 · see citing works

The role of phenomics and genomics in delineating the genetic basis of complex traits in millets

Jadhav, Y., Thakur, N. R., Ingle, K. P., & Ceasar, S. A. (2024) · Physiologia Plantarum · doi:10.1111/ppl.14349 · Cited by 27 · see citing works

Quantitative Evaluation of Color, Firmness, and Soluble Solid Content of Korla Fragrant Pears via IRIV and LS-SVM

Liu, Y., Wang, T., Su, R., Hu, C., Chen, F., & Cheng, J. (2021) · Agriculture · doi:10.3390/agriculture11080731 · Cited by 27 · see citing works

Application of image-based phenotyping tools to identify QTL for in-field winter survival of winter wheat (Triticum aestivum L.)

Chen, Y., Sidhu, H. S., Kaviani, M., McElroy, M. S., Pozniak, C. J., & Navabi, A. (2019) · Theoretical and Applied Genetics · doi:10.1007/s00122-019-03373-6 · Cited by 26 · see citing works

High-throughput phenotyping of brachiaria grass seeds using free access tool for analyzing X-ray images

de Medeiros, A. D., da Silva, L. J., Pereira, M. D., Oliveira, A. M. S., & Dias, D. C. F. D. S. (2020) · Anais da Academia Brasileira de Ciências · doi:10.1590/0001-3765202020190209 · Cited by 26 · see citing works

Compact and low-cost THz QTDS system

Probst, T., Rehn, A., & Koch, M. (2015) · Optics Express · doi:10.1364/oe.23.021972 · Cited by 25 · see citing works

Scientific and technological advances in the development of sustainable disease management tools: a case study on kiwifruit bacterial canker

Santos, M. G., da Silva, M. N., Vasconcelos, M. W., & Carvalho, S. M. P. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1306420 · Cited by 25 · see citing works

Macro optical projection tomography for large scale 3D imaging of plant structures and gene activity

Lee, K. J. I., Calder, G. M., Hindle, C., Newman, J. L., Robinson, S. N., Avondo, J., & Coen, E. (2016) · Journal of Experimental Botany · doi:10.1093/jxb/erw452 · Cited by 25 · see citing works

Predicting macroalgal pigments (chlorophylla, chlorophyllb, chlorophylla + b, carotenoids) in various environmental conditions using high-resolution hyperspectral spectroradiometers

Vahtmäe, E., Kotta, J., Orav‐Kotta, H., Kotta, I., Pärnoja, M., & Kutser, T. (2017) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1399481 · Cited by 25 · see citing works

A belt for the cell: cellulosic wall thickenings and their role in morphogenesis of the 3D puzzle cells in walnut shells

Antreich, S. J., Xiao, N., Huss, J. C., & Gierlinger, N. (2021) · Journal of Experimental Botany · doi:10.1093/jxb/erab197 · Cited by 25 · see citing works

High-throughput phenotyping and AI technologies for deciphering crop resilience to heat stress

Kundu, S., Saini, D. K., Meena, R. K., Bahuguna, R. N., & Jagadish, S. V. K. (2024) · Plant Physiology Reports · doi:10.1007/s40502-024-00821-4 · Cited by 23 · see citing works

Application of Hyperspectral Imaging for Early Detection of Pathogen-Induced Stress in Cabbage as Case Study

Szechyńska‐Hebda, M., Hołownicki, R., Doruchowski, G., Sas, K., Puławska, J., Jarecka-Boncela, A., Ptaszek, M., & Włodarek, A. (2025) · Agronomy · doi:10.3390/agronomy15071516 · Cited by 22 · see citing works

A low-cost and open-source platform for automated imaging

Lien, M. R., Barker, R. J., Ye, Z., Westphall, M. H., Gao, R., SINGH, A. K., Gilroy, S., & Townsend, P. A. (2019) · Plant Methods · doi:10.1186/s13007-019-0392-1 · Cited by 22 · see citing works

Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels

Maciel, G. M., Gallis, R. B. D. A., Barbosa, R. L., Pereira, L. M., Siquieroli, A. C. S., & Peixoto, J. V. M. (2020) · Bragantia · doi:10.1590/1678-4499.20190519 · Cited by 22 · see citing works

Differences in carbon isotope discrimination between angiosperm and gymnosperm woody plants, and their geological significance

Hare, V., & Lavergne, A. (2021) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2021.02.029 · Cited by 22 · see citing works

Supplementary Light with Increased Blue Fraction Accelerates Emergence and Improves Development of the Inflorescence in Aechmea, Guzmania and Vriesea

Asayesh, E. J., Aliniaeifard, S., Askari, N., Roozban, M. R., Sobhani, M., Tsaniklidis, G., Woltering, E. J., & Fanourakis, D. (2021) · Horticulturae · doi:10.3390/horticulturae7110485 · Cited by 22 · see citing works

Systems biology of eukaryotic superorganisms and the holobiont concept

Kutschera, U. (2018) · Theory in Biosciences · doi:10.1007/s12064-018-0265-6 · Cited by 21 · see citing works

Super-resolution expansion microscopy in plant roots

Gallei, M., Truckenbrodt, S., Kreuzinger, C., Inumella, S., Vistunou, V., Sommer, C., Tavakoli, M. R., Dueñas, N. A., Vorlaufer, J., Jahr, W., et al. (2025) · The Plant Cell · doi:10.1093/plcell/koaf006 · Cited by 20 · see citing works

Continuous monitoring of plant sodium transport dynamics using clinical PET

Ruwanpathirana, G. P., Plett, D., Williams, R. C., Davey, C. E., Johnston, L. A., & Kronzucker, H. J. (2021) · Plant Methods · doi:10.1186/s13007-021-00707-8 · Cited by 19 · see citing works

METHODOLOGY Open Access

Cendrero-mateo, M. P., & Jansen, M. (2016)

Phenotyping of barley resistance to plant diseases on the leaf and greenhouse scale through hyperspectral imaging and data analysis

Thomas, S. (2021) · bonndoc (University of Bonn)

Hyperspectral reflectance system for plant diagnostics

Klimenko, D. N., Гурова, Т. А., Elkin, O. V., & Maksimov, L. V. (2020) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/548/3/032019 · Cited by 2 · see citing works

Corn chlorophyll content detection based on spectral reflectance inversion absorptance

Song, D., & Kamruzzaman, M. (2024) · ASABE Annual International Meeting · doi:10.13031/aim.202400699

Evaluation of the benefits of combined reflection and transmission hyperspectral imaging data through disease detection and quantification in plant–pathogen interactions

Thomas, S., Behmann, J., Rascher, U., & Mahlein, A. (2022) · Journal of Plant Diseases and Protection · doi:10.1007/s41348-022-00570-2 · Cited by 7 · see citing works