References
- 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
- 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
- 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).
- 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
- 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).
- 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
- 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
- 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
- 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
- Barnhart, I. (2020). High-resolution UAS multispectral imaging for cultivar selection in grain sorghum breeding trials (Doctoral dissertation).
- 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
- 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.
- 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
- 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
- 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
- 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.
- 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
- 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).
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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).
- Delgado Fajardo, C. C. (2018). Multispectral image quality assessment to enhance classification rates of rice hojablanca virus (RHBV) in rice breeding programs (Doctoral dissertation).
- 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
- 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
- Dorrington, G. E. 2005. Development of an airship for tropical rain forest canopy exploration. Aeronautical Journal, 109(1098), 361–372.
- 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
- 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
- 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
- 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
- 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
- FAO. (2009). How to Feed the World in 2050. Rome, Italy: Food and Agriculture Organization.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Kalaji, M. H., Guo, P. (2008). Chlorophyll fluorescence: a useful tool in barley plant breeding programs. Photochemistry research progress, 29, 439–463.
- 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
- 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
- Khan, A., Sovero, V., Gemenet, D. (2016). Genome-assisted breeding for drought resistance. Current Genomics, 17(4), 330–342.10.2174/1389202917999160211101417495503527499682
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- Li, L., Zhang, Q., Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors, 14(11), 20078–20111.10.3390/s141120078427947225347588
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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).
- 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
- 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.
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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).
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zovko, M., Boras, I., Švaić, S. (2018). Assessing plant water status from infrared thermography for irrigation management. Proc. 14th Quantitative Infrared Thermography Conference.10.21611/qirt.2018.050
- 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).