A Review of Imaging and Sensing Technologies for Field Phenotyping

Agati, G., Traversi, M. L., Cerovic, Z. G. (2008). Chlorophyll fluorescence imaging for the non-invasive assessment of anthocyanins in whole grape (Vitis vinifera L.) bunches. Photochem. Photobiol., 84(6), 1431–1434.10.1111/j.1751-1097.2008.00424.x Search in Google Scholar

Aleksandrov, V. (2019). Identification of nutrient deficiency in bean plants by prompt chlorophyll fluorescence measurements and Artificial Neural Networks. https://doi.org/10.1101/66423510.1101/664235 Search in Google Scholar

Aleshko, R., Bogdanov, A., Shoshina, K., Ilintsev, A. (2020). Development of methods for automated determination of forest resource parameters using multispectral survey data from unmanned aerial vehicles. IOP Conference Series: Earth and Environmental. IOP Publishing. Science (pp. 012002). Search in Google Scholar

Araus, J. L., Slafer, G. A., Reynolds, M. P., Royo, C. (2002). Plant breeding and drought in C3 cereals: what should we breed for? Annals of botany, 89(7), 925–940.10.1093/aob/mcf049 Search in Google Scholar

Ashraf, M. A., Maah, M. J., Yusoff, I. (2011). Introduction to remote sensing of biomass. Biomass and remote sensing of biomass. IntechOpen (pp. 129–170). Search in Google Scholar

Awad, M. M. (2018). Forest mapping: A comparison between hyperspectral and multispectral images and technologies. Journal of Forestry Research, 29(5), 1395–1405.10.1007/s11676-017-0528-y Search in Google Scholar

Ballester, C., Brinkhoff, J., Quayle, W. C., Hornbuckle, J. (2019). Monitoring the effects of water stress in cotton using the green red vegetation index and red edge rRatio. Remote Sensing, 11(7), 873.10.3390/rs11070873 Search in Google Scholar

Baluja, J., Diago, M. P., Goovaerts, P., Tardaguila, J. (2012). Assessment of the spatial variability of anthocyanins in grapes using a fluorescence sensor: relationships with vine vigour and yield. Precision Agriculture, 13(4), 457.10.1007/s11119-012-9261-x Search in Google Scholar

Banks, J. M. (2018). Chlorophyll fluorescence as a tool to identify drought stress in Acer genotypes. Environmental and experimental botany, 155, 118–127.10.1016/j.envexpbot.2018.06.022 Search in Google Scholar

Barnhart, I. (2020). High-resolution UAS multispectral imaging for cultivar selection in grain sorghum breeding trials (Doctoral dissertation). Search in Google Scholar

Becklin, K. M., Anderson, J. T., Gerhart, L. M., Wadgymar, S. M., Wessinger, C. A., Ward, J. K. (2016). Examining plant physiological responses to climate change through an evolutionary lens. Plant physiology, 172(2), 635–649.10.1104/pp.16.00793 Search in Google Scholar

Benet, B., Dubos, C., Maupas, F., Malatesta, G., Lenain, R. (2018). Development of autonomous robotic platforms for sugar beet crop phenotyping using artificial vision. AGENG Conference, July 2018, Wageningen, NLD. Search in Google Scholar

Betemps, D. L., Fachinello, J. C., Galarça, S. P., Portela, N. M., Remorini, D., Massai, R., Agati, G. (2012). Non-destructive evaluation of ripening and quality traits in apples using a multiparametric fluorescence sensor. Journal of the Science of Food and Agriculture, 92(9), 1855–1864.10.1002/jsfa.5552 Search in Google Scholar

Boegh, E., Soegaard, H., Thomsen, A. (2002). Evaluating evapotranspiration rates and surface conditions using Landsat TM to estimate atmospheric resistance and surface resistance. Remote Sensing of Environment, 79(2–3), 329–343.10.1016/S0034-4257(01)00283-8 Search in Google Scholar

Briglia, N., Montanaro, G., Petrozza, A., Summerer, S., Cellini, F., Nuzzo, V. (2019). Drought phenotyping in Vitis vinifera using RGB and NIR imaging. Scientia Horticulturae, 256, 108555.10.1016/j.scienta.2019.108555 Search in Google Scholar

Bruinsma, J. (2009). The resource outlook to 2050: by how much do land, water and crop yields need to increase by 2050. Expert meeting on how to feed the world in, 2050, 24–26. Search in Google Scholar

Burkart, A., Hecht, V. L., Kraska, T., Rascher, U. (2018). Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution. Precision agriculture, 19(1), 134–146.10.1007/s11119-017-9504-y Search in Google Scholar

Buschmann, C., Langsdorf, G., Lichtenthaler, H. K. (2008). Blue, green, red, and far-red fluorescence signatures of plant tissues, their multicolor fluorescence imaging, and application for agrofood assessment. Optical monitoring of fresh and processed agricultural crops (pp. 272). Search in Google Scholar

Bürling, K., Cerovic, Z. G., Cornic, G., Ducruet, J. M., Noga, G., Hunsche, M. (2013). Fluorescence-based sensing of drought-induced stress in the vegetative phase of four contrasting wheat genotypes. Environmental and Experimental Botany, 89, 51–59.10.1016/j.envexpbot.2013.01.003 Search in Google Scholar

Cabrera-Bosquet, L., Molero, G., Stellacci, A., Bort, J., Nogues, S., Araus, J. (2011). NDVI as a potential tool for predicting biomass, plant nitrogen content and growth in wheat genotypes subjected to different water and nitrogen conditions. Cereal Research Communications, 39 (1), 147–159.10.1556/CRC.39.2011.1.15 Search in Google Scholar

Camino, C., González-Dugo, V., Hernández, P., Sillero, J. C., Zarco-Tejada, P. J. (2018). Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. International journal of applied earth observation and geoinformation, 70, 105–117.10.1016/j.jag.2018.04.013 Search in Google Scholar

Chelladurai, V., Jayas, D. S., White, N. D. G. (2010). Thermal imaging for detecting fungal infection in stored wheat. Journal of stored products research, 46(3), 174–179.10.1016/j.jspr.2010.04.002 Search in Google Scholar

Chen, Z., Yu, G., Yan, J., Wang, H. (2019). Contrasting temperature and precipitation patterns of trees in different seasons and responses of infrared canopy temperature in two asian subtropical forests. Forests, 10(10), 902.10.3390/f10100902 Search in Google Scholar

Christensen, L. K., Rodriguez, D., Belford, R., Sadras, V., Rampant, P., Fisher, P. (2005). Temporal prediction of nitrogen status in wheat under the influence of water deficiency using spectral and thermal information. Precision Agriculture, 5, 209–215. Search in Google Scholar

Cruzan, M. B., Weinstein, B. G., Grasty, M. R., Kohrn, B. F., Hendrickson, E. C., Arredondo, T. M., Thompson, P. G. (2016). Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology. Applications in plant sciences, 4(9), 1600041.10.3732/apps.1600041503336227672518 Search in Google Scholar

De Castro, A. I., Ehsani, R., Ploetz, R., Crane, J. H., Abdulridha, J. (2015). Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado. Remote Sensing of Environment, 171, 33–44.10.1016/j.rse.2015.09.011 Search in Google Scholar

De Bei, R., Fuentes, S., Wirthensohn, M. G., Cozzolino, D., Tyerman, S. D. (2017). Feasibility study on the use of Near Infrared spectroscopy to measure water status of almond trees. VII International Symposium on Almonds and Pistachios, 1219 (pp. 79–84). Search in Google Scholar

Delgado Fajardo, C. C. (2018). Multispectral image quality assessment to enhance classification rates of rice hojablanca virus (RHBV) in rice breeding programs (Doctoral dissertation). Search in Google Scholar

Ding, L., Dong, D., Jiao, L., Zheng, W. (2017). Potential using of infrared thermal imaging to detect volatile compounds released from decayed grapes. PloS one, 12(6), 0180649.10.1371/journal.pone.0180649549342828665984 Search in Google Scholar

Dong, D., Jiao, L., Li, C., Zhao, C. (2019). Rapid and real-time analysis of volatile compounds released from food using infrared and laser spectroscopy. TrAC Trends in Analytical Chemistry, 110, 410–416.10.1016/j.trac.2018.11.039 Search in Google Scholar

Dorrington, G. E. 2005. Development of an airship for tropical rain forest canopy exploration. Aeronautical Journal, 109(1098), 361–372. Search in Google Scholar

Egea, G., Padilla-Díaz, C. M., Martinez-Guanter, J., Fernández, J. E., Pérez-Ruiz, M. (2017). Assessing a crop water stress index derived from aerial thermal imaging and infrared thermometry in super-high density olive orchards. Agricultural Water Management, 187, 210–221.10.1016/j.agwat.2017.03.030 Search in Google Scholar

Eguchi, A., Konishi, A., Hosoi, F., Omasa, K. (2008). Three-dimensional chlorophyll fluorescence imaging for detecting effects of herbicide on a whole plant. Photosynthesis. Energy from the Sun. Springer, Dordrecht, 2008. pp. 577–580.10.1007/978-1-4020-6709-9_130 Search in Google Scholar

ElMasry, G., Elgamal, R., Mandour, N., Gou, P., Al-Rejaie, S., Belin, E., Rousseau, D. (2020). Emerging thermal imaging techniques for seed quality evaluation: Principles and applications. Food Research International, 131, 109025.10.1016/j.foodres.2020.10902532247450 Search in Google Scholar

Elsayed, S., Elhoweity, M., Ibrahim, H. H., Dewir, Y. H., Migdadi, H. M., Schmidhalter, U. (2017). Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agricultural Water Management, 189, 98–110.10.1016/j.agwat.2017.05.001 Search in Google Scholar

Enders, T. A., St. Dennis, S., Oakland, J., Callen, S. T., Gehan, M. A., Miller, N. D., Spalding, E. P., Springer, N. M., Hirsch, C. D. (2019). Classifying cold-stress responses of inbred maize seedlings using RGB imaging. Plant direct, 3(1), 00104.10.1002/pld3.104650884031245751 Search in Google Scholar

FAO. (2009). How to Feed the World in 2050. Rome, Italy: Food and Agriculture Organization. Search in Google Scholar

Feng, W., Yao, X., Zhu, Y., Tian, Y. C., Cao, W. X. (2008). Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. European Journal of Agronomy, 28(3), 394–404.10.1016/j.eja.2007.11.005 Search in Google Scholar

Fernandez-Gallego, J. A., Kefauver, S. C., Vatter, T., Gutiérrez, N. A., Nieto-Taladriz, M. T., Araus, J. L. (2019). Low-cost assessment of grain yield in durum wheat using RGB images. European Journal of Agronomy, 105, 146–156.10.1016/j.eja.2019.02.007 Search in Google Scholar

Franklin, S. E. (2018). Pixel-and object-based multispectral classification of forest tree species from small unmanned aerial vehicles. Journal of Unmanned Vehicle Systems, 6(4), 195–211.10.1139/juvs-2017-0022 Search in Google Scholar

Fu, P., Meacham-Hensold, K., Guan, K., Bernacchi, C. J. (2019). Hyperspectral leaf reflectance as proxy for photosynthetic capacities: An ensemble approach based on multiple machine learning algorithms. Frontiers in Plant Science, 10, 730.10.3389/fpls.2019.00730655651831214235 Search in Google Scholar

García-Santillán, I. D., Montalvo, M., Guerrero, J. M., Pajares, G. (2017). Automatic detection of curved and straight crop rows from images in maize fields. Biosystems Engineering, 156, 61–79.10.1016/j.biosystemseng.2017.01.013 Search in Google Scholar

García-Tejero, I. F., Rubio, A. E., Viñuela, I., Hernández, A., Gutiérrez-Gordillo, S., Rodríguez-Pleguezuelo, C. R., Durán-Zuazo, V. H. (2018). Thermal imaging at plant level to assess the crop-water status in almond trees (cv. Guara) under deficit irrigation strategies. Agricultural Water Management, 208, 176–186.10.1016/j.agwat.2018.06.002 Search in Google Scholar

Ge, Y., Bai, G., Stoerger, V., Schnable, J. C. (2016). Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture, 127, 625–632.10.1016/j.compag.2016.07.028 Search in Google Scholar

Ghosh, P., Rana, S. S., Nayak, A., Pradhan, R. C. (2016). Quality evaluation of food by thermal imaging. Internat. J. Proc. & Post Harvest Technol., 7(1), 126–133.10.15740/HAS/IJPPHT/7.1/126-133 Search in Google Scholar

Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812–818.10.1126/science.118538320110467 Search in Google Scholar

Golzarian, M. R., Frick, R. A., Rajendran, K., Berger, B., Roy, S., Tester, M., Lun, D. S. (2011). Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant methods, 7(1), 2.10.1186/1746-4811-7-2304298621284859 Search in Google Scholar

Guo, W., Fukatsu, T., Ninomiya, S. (2015). Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images. Plant methods, 11(1), 7.10.1186/s13007-015-0047-9433672725705245 Search in Google Scholar

Hallik, L., Kazantsev, T., Kuusk, A., Galmés, J., Tomás, M., Niinemets, Ü. (2017). Generality of relationships between leaf pigment contents and spectral vegetation indices in Mallorca (Spain). Regional Environmental Change, 17(7), 2097–2109.10.1007/s10113-017-1202-9 Search in Google Scholar

Hasanuzzaman, M., Nahar, K., Alam, M., Roychowdhury, R., Fujita, M. (2013). Physiological, biochemical, and molecular mechanisms of heat stress tolerance in plants. International journal of molecular sciences, 14(5), 9643–9684.10.3390/ijms14059643367680423644891 Search in Google Scholar

He, L., Chen, J. M., Liu, J., Zheng, T., Wang, R., Joiner, J., Chou, S., Chen, B., Liu, Y., Liu, R., Rogers, C. (2019). Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements. Remote Sensing of Environment, 232, 111344.10.1016/j.rse.2019.111344760805133149371 Search in Google Scholar

Hernández-Clemente, R., North, P. R., Hornero, A., Zarco-Tejada, P. J. (2017). Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sensing of Environment, 193, 165–179.10.1016/j.rse.2017.02.012 Search in Google Scholar

Hniličková, H., Hnilička, F., Martinková, J., Kraus, K. (2017). Effects of salt stress on water status, photosynthesis and chlorophyll fluorescence of rocket. Plant, Soil and Environment, 63(8), 362–367.10.17221/398/2017-PSE Search in Google Scholar

Hou, W., Sun, A. H., Chen, H. L., Yang, F. S., Pan, J. L., Guan, M. Y. (2016). Effects of chilling and high temperatures on photosynthesis and chlorophyll fluorescence in leaves of watermelon seedlings. Biologiaplantarum, 60(1), 148–154.10.1007/s10535-015-0575-1 Search in Google Scholar

Huang, S., Wang, L., Liu, L., Fu, Q., Zhu, D. (2014). Nonchemical pest control in China rice: a review. Agronomy for sustainable development, 34(2), 275–291.10.1007/s13593-013-0199-9 Search in Google Scholar

Huang, Y., Reddy, K. N., Fletcher, R. S., Pennington, D. (2018). UAV low-altitude remote sensing for precision weed management. Weed technology, 32(1), 2–6.10.1017/wet.2017.89 Search in Google Scholar

Humplík, J. F., Lazár, D., Husičková, A., Spíchal, L. (2015). Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review. Plant methods, 11(1), 29.10.1186/s13007-015-0072-8440617125904970 Search in Google Scholar

Hwang, K., Chandler, D. G., Shaw, S. B. (2020). Patch scale evapotranspiration of wetland plant species by ground-based infrared thermometry. Agricultural and Forest Meteorology, 287, 107948.10.1016/j.agrformet.2020.107948 Search in Google Scholar

Kalaji, M. H., Guo, P. (2008). Chlorophyll fluorescence: a useful tool in barley plant breeding programs. Photochemistry research progress, 29, 439–463. Search in Google Scholar

Kalaji, H. M., Jajoo, A., Oukarroum, A., Brestic, M., Zivcak, M., Samborska, I. A., Cetner, M. D., Łukasik, I., Goltsev, V., Ladle, R. J. (2016). Chlorophyll a fluorescence as a tool to monitor physiological status of plants under abiotic stress conditions. Actaphysiologiaeplantarum, 38(4), 102.10.1007/s11738-016-2113-y Search in Google Scholar

Kalisperakis, I., Stentoumis, C., Grammatikopoulos, L., Karantzalos, K. (2015). Leaf area index estimation in vineyards from UAV hyperspectral data, 2D image mosaics and 3D canopy surface models. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 299.10.5194/isprsarchives-XL-1-W4-299-2015 Search in Google Scholar

Khan, A., Sovero, V., Gemenet, D. (2016). Genome-assisted breeding for drought resistance. Current Genomics, 17(4), 330–342.10.2174/1389202917999160211101417495503527499682 Search in Google Scholar

Khorsandi, A., Hemmat, A., Mireei, S. A., Amirfattahi, R., Ehsanzadeh, P. (2018). Plant temperature-based indices using infrared thermography for detecting water status in sesame under greenhouse conditions. Agricultural Water Management, 204, 222–233.10.1016/j.agwat.2018.04.012 Search in Google Scholar

Kirchgessner, N., Liebisch, F., Yu, K., Pfeifer, J., Friedli, M., Hund, A., Walter, A. (2017). The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system. Functional Plant Biology, 44(1), 154–168.10.1071/FP16165 Search in Google Scholar

Kleefeld, A., Gypser, S., Herppich, W. B., Bader, G., Veste, M. (2018). Identification of spatial pattern of photosynthesis hotspots in moss-and lichen-dominated biological soil crusts by combining chlorophyll fluorescence imaging and multispectral BNDVI images. Pedobiologia, 68, 1–11.10.1016/j.pedobi.2018.04.001 Search in Google Scholar

Klosterman, S., Richardson, A. D. (2017). Observing spring and fall phenology in a deciduous forest with aerial drone imagery. Sensors, 17(12), 2852.10.3390/s17122852 Search in Google Scholar

Knipling, E. B. (1970). Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote sensing of environment, 1(3), 155–159.10.1016/S0034-4257(70)80021-9 Search in Google Scholar

Kovar, M., Brestic, M., Zivcak, M., Olsovska, K., Sytar, O., Botyanszka, L., Chovancek, E., Bárek, V. (2018). Hyperspectral imaging-tool for non-destructive evaluation of water content in plant (Conference poster). Vlivabiotických a Biotických stresorů na vlastnosti rostlin, 5, 135–138. Search in Google Scholar

Kovar, M., Brestic, M., Sytar, O., Barek, V., Hauptvogel, P., Zivcak, M. (2019). Evaluation of hyperspectral reflectance parameters to assess the leaf water content in soybean. Water, 11(3), 443.10.3390/w11030443 Search in Google Scholar

Kuzy, J., Jiang, Y., Li, C. (2018). Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biology and Technology, 136, 166–177.10.1016/j.postharvbio.2017.10.011 Search in Google Scholar

Leipner, J., Oxborough, K., Baker, N. R. (2001). Primary sites of ozone-induced perturbations of photosynthesis in leaves: identification and characterization in Phaseolus vulgaris using high resolution chlorophyll fluorescence imaging. Journal of Experimental Botany, 52(361), 1689-1696.10.1093/jxb/52.361.1689 Search in Google Scholar

Lenthe, J. H., Oerke, E. C., Dehne, H. W. (2007). Digital infrared thermography for monitoring canopy health of wheat. Precision Agriculture, 8(1–2), 15–26.10.1007/s11119-006-9025-6 Search in Google Scholar

Leuzinger, S., Körner, C. (2007). Tree species diversity affects canopy leaf temperatures in a mature temperate forest. Agricultural and forest meteorology, 146(1–2), 29–37.10.1016/j.agrformet.2007.05.007 Search in Google Scholar

Li, L., Zhang, Q., Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078–20111.10.3390/s141120078427947225347588 Search in Google Scholar

Li, H., Malik, M. H., Gao, Y., Qiu, R., Miao, Y., Zhang, M. (2018). Maize plant water stress detection based on RGB image and thermal infrared image. 2018 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers (pp. 1).10.13031/aim.201800474 Search in Google Scholar

Liu, M., Liu, X., Ding, W., Wu, L. (2011). Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis. International Journal of Applied Earth Observation and Geoinformation, 13(2), 246–255.10.1016/j.jag.2010.12.006 Search in Google Scholar

Liu, T., Wu, W., Chen, W., Sun, C., Zhu, X., Guo, W. (2016). Automated image-processing for counting seedlings in a wheat field. Precision agriculture, 17(4), 392–406.10.1007/s11119-015-9425-6 Search in Google Scholar

Long, S. P., Zhu, X. G., Naidu, S. L., Ort, D. R. (2006). Can improvement in photosynthesis increase crop yields? Plant, cell & environment, 29(3), 315–330.10.1111/j.1365-3040.2005.01493.x17080588 Search in Google Scholar

Luus, K. A., Commane, R., Parazoo, N. C., Benmergui, J., Euskirchen, E. S., Frankenberg, C., Zona, D., Joiner, J., Lindaas, J., Miller, C. E., Oechel, W. C., Wofsy, S., Lin, J. C. (2017). Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence. Geophysical Research Letters, 44(3), 1564–1573.10.1002/2016GL070842 Search in Google Scholar

Mahlein, A. K., Alisaac, E., Al Masri, A., Behmann, J., Dehne, H. W., Oerke, E. C. (2019). Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring fusarium head blight of wheat on spikelet scale. Sensors, 19(10), 2281.10.3390/s19102281656788531108868 Search in Google Scholar

Medina, I., Newton, E., Kearney, M. R., Mulder, R. A., Porter, W. P. – Stuart-Fox, D. (2018). Reflection of near-infrared light confers thermal protection in birds. Nature communications, 9(1), 1–7.10.1038/s41467-018-05898-8612731030190466 Search in Google Scholar

Miao, G., Guan, K., Yang, X., Bernacchi, C. J., Berry, J. A., Delucia, E. H., Wu, J., Moore, C. E., Meacham, K., Cai, Y., Kimm, H., Masters, M. D. Peng, B. (2018). Sun-induced chlorophyll fluorescence, photosynthesis, and light use efficiency of a soybean field from seasonally continuous measurements. Journal of Geophysical Research: Biogeosciences, 123(2), 610–623.10.1002/2017JG004180 Search in Google Scholar

Mistele, B., Schmidhalter, U. (2008). Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. European Journal of Agronomy, 29(4), 184–190.10.1016/j.eja.2008.05.007 Search in Google Scholar

Moghimi, A., Yang, C., Miller, M. E., Kianian, S. F., Marchetto, P. M. (2018). A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Frontiers in plant science, 9, 1182.10.3389/fpls.2018.01182611750730197650 Search in Google Scholar

Moghimi, A., Yang, C., Anderson, J. A., Reynolds, S. K. (2019). Selecting informative spectral bands using machine learning techniques to detect Fusarium head blight in wheat. 2019 ASABE Annual International Meeting (pp. 1).10.13031/aim.201900815 Search in Google Scholar

Montero, R., Pérez-Bueno, M. L., Barón, M., Florez-Sarasa, I., Tohge, T., Fernie, A. R.,, El AouOuad, H., Flexas, J., Bota, J. (2016). Alterations in primary and secondary metabolism in Vitis vinifera ‘Malvasía de Banyalbufar’upon infection with Grapevine leafroll-associated virus 3. Physiologiaplantarum, 157(4), 442–452.10.1111/ppl.12440 Search in Google Scholar

Morales, F., Ancín, M., Fakhet, D., González-Torralba, J., Gámez, A. L., Seminario, A., Soba, D., Ben Mariem, S., Garriga, M., Aranjuelo, I. (2020). Photosynthetic Metabolism under Stressful Growth Conditions as a Bases for Crop Breeding and Yield Improvement. Plants, 9(1), 88.10.3390/plants9010088702042431936732 Search in Google Scholar

Noble, E., Kumar, S., Görlitz, F. G., Stain, C., Dunsby, C., French, P. M. (2017). In vivo label-free mapping of the effect of a photosystem II inhibiting herbicide in plants using chlorophyll fluorescence lifetime. Plant methods, 13(1), 48.10.1186/s13007-017-0201-7547297628638436 Search in Google Scholar

Nowak, M. M., Dziób, K., Bogawski, P. (2019). Unmanned Aerial Vehicles (UAVs) in environmental biology: a review. European Journal of Ecology, 4(2), 56–74.10.2478/eje-2018-0012 Search in Google Scholar

Nowakowski, A. J., Frishkoff, L. O., Agha, M., Todd, B. D., Scheffers, B. R. (2018). Changing thermal landscapes: merging climate science and landscape ecology through thermal biology. Current Landscape Ecology Reports, 3(4), 57–72.10.1007/s40823-018-0034-8 Search in Google Scholar

Oerke, E. C., Mahlein, A. K., Steiner, U. (2014). Proximal sensing of plant diseases. Detection and Diagnostics of Plant Pathogens. Dordrecht: Springer (pp. 55–68). Search in Google Scholar

Omran, E. S. E. (2017). Early sensing of peanut leaf spot using spectroscopy and thermal imaging. Archives of Agronomy and Soil Science, 63(7), 883–896.10.1080/03650340.2016.1247952 Search in Google Scholar

Parfitt, J., Barthel, M., Macnaughton, S. (2010). Food waste within food supply chains: quantification and potential for change to 2050. Philosophical transactions of the royal society B: biological sciences, 365(1554), 3065–3081. Search in Google Scholar

Parihar, G., Praveen, S., Padgaonkar, R., Giri, L. I. (2020). Infrared thermography based smart irrigation scheduling for horticulture plants. Thermosense: Thermal Infrared Applications XLII International Society for Optics and Photonics, 11409, 1140908. Search in Google Scholar

Park, E., Hong, S. J., Lee, A. Y., Park, J., Cho, B. K., Kim, G. (2017). Phenotyping of low-temperature stressed pepper seedlings using infrared thermography. Journal of Biosystems Engineering, 42(3), 163–169. Search in Google Scholar

Pérez-Bueno, M. L., Pineda, M., Cabeza, F. M., Barón, M. (2016). Multicolor fluorescence imaging as a candidate for disease detection in plant phenotyping. Frontiers in plant science, 7, 1790.10.3389/fpls.2016.01790513435427994607 Search in Google Scholar

Poirier-Pocovi, M., Volder, A., Bailey, B. N. (2020). Modeling of reference temperatures for calculating crop water stress indices from infrared thermography. Agricultural Water Management, 233, 106070.10.1016/j.agwat.2020.106070 Search in Google Scholar

Potgieter, A. B., George-Jaeggli, B., Chapman, S. C., Laws, K., Suárez Cadavid, L. A., Wixted, J., Watson, J., Eldridge, M., Jordan, D. R., Hammer, G. L. (2017). Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Frontiers in Plant Science, 8, 1532.10.3389/fpls.2017.01532559977228951735 Search in Google Scholar

Prabhakara, K., Hively, W. D., McCarty, G. W. (2015). Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States. International journal of applied earth observation and geoinformation, 39, 88–102.10.1016/j.jag.2015.03.002 Search in Google Scholar

Pushpavalli, R., Kanatti, A., Govindaraj, M. (2020). Use of infrared thermography imaging for assessing heat tolerance in high and low iron pearl millet lines. Electronic Journal of Plant Breeding, 11(2), 626–632. Search in Google Scholar

Putra, B. T. W., Soni, P., Marhaenanto, B., Harsono, S. S., Fountas, S. (2020). Using information from images for plantation monitoring: A review of solutions for smallholders. Information Processing in Agriculture, 7(1), 109–11910.1016/j.inpa.2019.04.005 Search in Google Scholar

Qiu, Q., Sun, N., Bai, H., Wang, N., Fan, Z., Wang, Y., Fan, Z., Wang, Y., Meng1, Z., Li1, B.- Cong, Y. (2019). Field-based high-throughput phenotyping for Maize plant using 3D LiDAR point cloud generated with a “Phenomobile”. Frontiers in plant science, 10(219), 554.10.3389/fpls.2019.00554651437731134110 Search in Google Scholar

Rahaman, M., Chen, D., Gillani, Z., Klukas, C., Chen, M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Frontiers in plant science, 6, 619.10.3389/fpls.2015.00619453059126322060 Search in Google Scholar

Ranđelović, P., Đorđević, V., Milić, S., Balešević-Tubić, S., Petrović, K., Miladinović, J., Đukić, V. (2020). Prediction of soybean plant density using a machine learning model and vegetation indices extracted from RGB images taken with a UAV. Agronomy, 10(8), 1108.10.3390/agronomy10081108 Search in Google Scholar

Raeva, P. L., Šedina, J., Dlesk, A. (2019). Monitoring of crop fields using multispectral and thermal imagery from UAV. European Journal of Remote Sensing, 52, 192–201.10.1080/22797254.2018.1527661 Search in Google Scholar

Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., Kim, D. (2015). Methods of integrating data to uncover genotype-phenotype interactions. Nature Reviews Genetics, 16(2), 85–97.10.1038/nrg386825582081 Search in Google Scholar

Rolfe, S. A., Scholes, J. D. (2010). Chlorophyll fluorescence imaging of plant-pathogen interactions. Protoplasma, 247(3–4), 163–175.10.1007/s00709-010-0203-z20814703 Search in Google Scholar

Sadeghi-Tehran, P., Virlet, N., Sabermanesh, K., Hawkesford, M. J. (2017). Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping. Plant methods, 13(1), 103.10.1186/s13007-017-0253-8569677529201134 Search in Google Scholar

Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K. T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., Pauli, D., Fritschi, F., Shakoor, N., Mockler, T., Ward, R. (2019). UAV-based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640, and thermomap cameras. Remote Sensing, 11(3), 330.10.3390/rs11030330 Search in Google Scholar

Sallam, A., Alqudah, A. M., Dawood, M. F., Baenziger, P. S., Börner, A. (2019). Drought stress tolerance in wheat and barley: advances in physiology, breeding and genetics research. International journal of molecular sciences, 20(13), 3137.10.3390/ijms20133137665178631252573 Search in Google Scholar

Sanchez, P. D. C., Hashim, N., Shamsudin, R., Nor, M. Z. M. (2020). Applications of imaging and spectroscopy techniques for nondestructive quality evaluation of potatoes and sweet potatoes: A review. Trends in Food Science & Technology, 96, 208–221.10.1016/j.tifs.2019.12.027 Search in Google Scholar

Santesteban, L. G., Di Gennaro, S. F., Herrero-Langreo, A., Miranda, C., Royo, J. B., Matese, A. (2017). High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, 183, 49–5910.1016/j.agwat.2016.08.026 Search in Google Scholar

Singh, A. S., Jones, A. M. P., Shukla, M. R., Saxena, P. K. (2017). High light intensity stress as the limiting factor in micropropagation of sugar maple (Acer saccharum Marsh.). Plant Cell, Tissue and Organ Culture, 129(2), 209–221.10.1007/s11240-017-1170-2 Search in Google Scholar

Simkó, A., Gáspár, G. S., Kiss, L., Makleit, P., Veres, S. (2020). Evaluation of nitrogen nutrition in diminishing water deficiency at different growth stages of maize by chlorophyll fluorescence parameters. Plants, 9(6), 676.10.3390/plants9060676735653832471090 Search in Google Scholar

Sirault, X. R., James, R. A., Furbank, R. T. (2009). A new screening method for osmotic component of salinity tolerance in cereals using infrared thermography. Functional Plant Biology, 36(11), 970–977.10.1071/FP0918232688708 Search in Google Scholar

Song, Q. H., Deng, Y., Zhang, Y. P., Deng, X. B., Lin, Y. X., Zhou, L. G., Fei, X., Sha, L., Liu, Y., Zhou, W., Gao, J. B. (2017). Comparison of infrared canopy temperature in a rubber plantation and tropical rain forest. International journal of biometeorology, 61(10), 1885–1892.10.1007/s00484-017-1375-428761981 Search in Google Scholar

Sonti, N. F., Hallett, R. A., Griffin, K. L., Trammell, T. L., Sullivan, J. H. (2020). Chlorophyll fluorescence parameters, leaf traits, and foliar chemistry of white oak and red maple trees in urban forest patches. Tree Physiology. Search in Google Scholar

Stagakis, S., Markos, N., Sykioti, O., Kyparissis, A. (2010). Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: An application on a Phlomisfruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment, 114(5), 977–994.10.1016/j.rse.2009.12.006 Search in Google Scholar

Stoyanova, M., Kandilarov, A., Koutev, V., Nitcheva, O., Dobreva, P. (2018). Potential of multispectral imaging technology for assessment coniferous forests bitten by a bark beetle in Central Bulgaria. MATEC Web of Conferences. EDP Sciences (pp. 01005).10.1051/matecconf/201814501005 Search in Google Scholar

Sugiura, R., Tsuda, S., Tamiya, S., Itoh, A., Nishiwaki, K., Murakami, N., Shibuyaa, Y., Hirafujiab, M., Nuske, S. (2016). Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosystems engineering, 148, 1–10.10.1016/j.biosystemseng.2016.04.010 Search in Google Scholar

Sytar, O., Brestic, M., Zivcak, M., Olsovska, K., Kovar, M., Shao, H.- He, X. (2017). Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Science of the Total Environment, 578, 90–99.10.1016/j.scitotenv.2016.08.01427524726 Search in Google Scholar

Sytar, O., Bruckova, K., Plotnitskaya, A., Zivcak, M., Brestic, M. (2019). Non-destructive methodology in comparative physiology of buckwheat genotypes within the different origin. Fagopyrum, 36(1), 11–21.10.3986/fag0007 Search in Google Scholar

Thorp, K. R., Thompson, A. L., Harders, S. J., French, A. N., Ward, R. W. (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sensing, 10(11), 1682.10.3390/rs10111682 Search in Google Scholar

Torres-Sánchez, J., Peña-Barragán, J. M., Gómez-Candón, D., De Castro, A. I., López-Granados, F. (2013). Imagery from unmanned aerial vehicles for early site specific weed management. Precision agriculture ’13. Wageningen: Wageningen Academic Publishers (pp. 193–199). Search in Google Scholar

Ubbens, J. R., Stavness, I. (2017). Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Frontiers in plant science, 8, 1190.10.3389/fpls.2017.01190550063928736569 Search in Google Scholar

Van Iersel, M. W., Mattos, E., Weaver, G., Ferrarezi, R. S., Martin, M. T., Haidekker, M. (2016). Using chlorophyll fluorescence to control lighting in controlled environment agriculture. VIII International Symposium on Light in Horticulture (pp. 427–434).10.17660/ActaHortic.2016.1134.54 Search in Google Scholar

Veys, C., Hibbert, J., Davis, P., Grieve, B. (2017). An ultra-low-cost active multispectral crop diagnostics device. IEEE SENSORS, 1–3.10.1109/ICSENS.2017.8234211 Search in Google Scholar

Wang, Y., Frei, M. (2011). Stressed food–The impact of abiotic environmental stresses on crop quality. Agriculture, Ecosystems & Environment, 141(3–4), 271–286.10.1016/j.agee.2011.03.017 Search in Google Scholar

Wang, L., Poque, S., Valkonen, J. P. (2019). Phenotyping viral infection in sweetpotato using a high-throughput chlorophyll fluorescence and thermal imaging platform. Plant methods, 15(1), pp. 116.10.1186/s13007-019-0501-1680536131649744 Search in Google Scholar

Watanabe, E., Fekih, R., Kasajima, I. (2019). Advances in Chlorophyll Fluorescence Theories: Close Investigation into Oxidative Stress and Potential Use for Plant Breeding. Redox Homeostasis in Plants. Springer, Cham (pp. 137–154).10.1007/978-3-319-95315-1_7 Search in Google Scholar

Weil, G., Lensky, I. M., Resheff, Y. S., Levin, N. (2017). Optimizing the timing of unmanned aerial vehicle image acquisition for applied mapping of woody vegetation species using feature selection. Remote Sensing, 9(1)1, 1130.10.3390/rs9111130 Search in Google Scholar

Xu, J., Lv, Y., Liu, X., Dalson, T., Yang, S., Wu, J. (2016). Diagnosing crop water stress of rice using infra-red thermal imager under water deficit condition. Int. J. Agric. Biol, 18, 565–572.10.17957/IJAB/15.0125 Search in Google Scholar

Yang, W., Feng, H., Zhang, X., Zhang, J., Doonan, J. H., Batchelor, W. D., Xiong, L., Yan, J. (2020). Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Molecular Plant, 13(2), 187–214.10.1016/j.molp.2020.01.00831981735 Search in Google Scholar

Yi, Q. X., Bao, A. M., Wang, Q., Zhao, J. (2013). Estimation of leaf water content in cotton by means of hyperspectral indices. Computers and electronics in agriculture, 90, 144–15110.1016/j.compag.2012.09.011 Search in Google Scholar

Young, S. N., Kayacan, E., Peschel, J. M. (2019). Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precision Agriculture, 20(4), 697–722.10.1007/s11119-018-9601-6 Search in Google Scholar

Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., Xu, B. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9(7), 708.10.3390/rs9070708 Search in Google Scholar

Zeng, X., Miao, Y., Ubaid, S., Gao, X., Zhuang, S. (2020). Detection and classification of bruises of pears based on thermal images. Postharvest Biology and Technology, 161, 111090.10.1016/j.postharvbio.2019.111090 Search in Google Scholar

Zhang, D., Zhou, X., Zhang, J., Lan, Y., Xu, C., Liang, D. (2018). Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PloS one, 13(5), 0187470.10.1371/journal.pone.0187470594503329746473 Search in Google Scholar

Zhang, J., Wang, C., Yang, C., Xie, T., Jiang, Z., Hu, T., Luo, Z., Zhou, G., Xie, J. (2020). Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring. Remote Sensing, 12(7), 1207.10.3390/rs12071207 Search in Google Scholar

Zhang, L., Zhang, H., Niu, Y., Han, W. (2019). Mapping maize water stress based on UAV multispectral remote sensing. Remote Sensing. 11(6), 605.10.3390/rs11060605 Search in Google Scholar

Zhao, D., Reddy, K. R., Kakani, V. G., Reddy, V. R. (2005). Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European journal of agronomy, 22(4), 391–403.10.1016/j.eja.2004.06.005 Search in Google Scholar

Zheng, H., Zhou, X., Cheng, T., Yao, X., Tian, Y., Cao, W., Zhu, Y. (2016). Evaluation of a UAV-based hyperspectral frame camera for monitoring the leaf nitrogen concentration in rice. In 2016 IEEE International Geoscience and Remote Sensing Symposium IEEE (pp. 7350–7353).10.1109/IGARSS.2016.7730917 Search in Google Scholar

Zovko, M., Boras, I., Švaić, S. (2018). Assessing plant water status from infrared thermography for irrigation management. Proc. 14^th Quantitative Infrared Thermography Conference.10.21611/qirt.2018.050 Search in Google Scholar

Zúñiga, C. E., Khot, L. R., Jacoby, P., Sankaran, S. (2016). Remote sensing based water-use efficiency evaluation in sub-surface irrigated wine grape vines. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping. International Society for Optics and Photonics, 9866 (pp. 98660). Search in Google Scholar