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Статті в журналах з теми "Plant reflectance spectra"
Steddom, K., G. Heidel, D. Jones, and C. M. Rush. "Remote Detection of Rhizomania in Sugar Beets." Phytopathology® 93, no. 6 (June 2003): 720–26. http://dx.doi.org/10.1094/phyto.2003.93.6.720.
Повний текст джерелаSchweiger, Anna K., Jeannine Cavender-Bares, Shan Kothari, Philip A. Townsend, Michael D. Madritch, Jake J. Grossman, Hamed Gholizadeh, Ran Wang, and John A. Gamon. "Coupling spectral and resource-use complementarity in experimental grassland and forest communities." Proceedings of the Royal Society B: Biological Sciences 288, no. 1958 (September 2021): 20211290. http://dx.doi.org/10.1098/rspb.2021.1290.
Повний текст джерелаKvíčala, Miroslav, Eva Lacková, and Michaela Štamborská. "Internal Reflectance Modelling ofHordeum vulgareLeaves During Drying." Journal of Chemistry 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/210679.
Повний текст джерелаMerrick, Trina, Ralf Bennartz, Maria Luisa S. P. Jorge, Stephanie Pau, and John Rausch. "Evaluation of Plant Stress Monitoring Capabilities Using a Portable Spectrometer and Blue-Red Grow Light." Sensors 22, no. 9 (April 29, 2022): 3411. http://dx.doi.org/10.3390/s22093411.
Повний текст джерелаZhu, Yan, Yingxue Li, Wei Feng, Yongchao Tian, Xia Yao, and Weixing Cao. "Monitoring leaf nitrogen in wheat using canopy reflectance spectra." Canadian Journal of Plant Science 86, no. 4 (October 10, 2006): 1037–46. http://dx.doi.org/10.4141/p05-157.
Повний текст джерелаStone, Christine, Laurie A. Chisholm, and Simon McDonald. "Spectral reflectance characteristics of Pinus radiata needles affected by dothistroma needle blight." Canadian Journal of Botany 81, no. 6 (June 1, 2003): 560–69. http://dx.doi.org/10.1139/b03-053.
Повний текст джерелаBasinger, Nicholas T., Katherine M. Jennings, Erin L. Hestir, David W. Monks, David L. Jordan, and Wesley J. Everman. "Phenology affects differentiation of crop and weed species using hyperspectral remote sensing." Weed Technology 34, no. 6 (August 18, 2020): 897–908. http://dx.doi.org/10.1017/wet.2020.92.
Повний текст джерелаSolovchenko, Alexei, Alexei Dorokhov, Boris Shurygin, Alexandr Nikolenko, Vitaly Velichko, Igor Smirnov, Dmitriy Khort, Aleksandr Aksenov, and Andrey Kuzin. "Linking Tissue Damage to Hyperspectral Reflectance for Non-Invasive Monitoring of Apple Fruit in Orchards." Plants 10, no. 2 (February 5, 2021): 310. http://dx.doi.org/10.3390/plants10020310.
Повний текст джерелаLi, Ying, Brian K. Via, Yaoxiang Li, and Guozhong Wang. "Determination of Geographical Origin and Tree Species Using Vis-NIR and Chemometric Methods." Forest Products Journal 72, no. 3 (May 1, 2022): 147–54. http://dx.doi.org/10.13073/fpj-d-22-00011.
Повний текст джерелаPandey, Jitendra Kumar, and R. Gopal. "Laser-induced chlorophyll fluorescence and reflectance spectroscopy of cadmium treatedTriticum aestivumL. plants." Spectroscopy 26, no. 2 (2011): 129–39. http://dx.doi.org/10.1155/2011/640232.
Повний текст джерелаДисертації з теми "Plant reflectance spectra"
Johansson, Peter. "Plant Condition Measurement from Spectral Reflectance Data." Thesis, Linköping University, Computer Vision, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59286.
Повний текст джерелаThe thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.
Bidston, Caroline. "The effects of metal pollution on the spectral reflectance of plants." Thesis, University of Reading, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314319.
Повний текст джерелаHollberg, Jens Lothar [Verfasser]. "Detecting Plant Functional Traits of Grassland Vegetation Using Spectral Reflectance Measurements / Jens Lothar Hollberg." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1160594171/34.
Повний текст джерелаSugianto, Biological Earth & Environmental Science UNSW. "Multi-angular hyperspectral data and its influences on soil and plant property measurements: spectral mapping and functional data analysis approach." Awarded by:University of New South Wales. Biological, Earth and Environmental Science, 2006. http://handle.unsw.edu.au/1959.4/25531.
Повний текст джерелаYang, Yang. "Non-contacting techniques for detecting plant drought stress in a closed environment." Connect to this title online, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1068499233.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xx, 245 p.; also includes graphics. Includes bibliographical references (p. 206-216).
Chen, Yaw-Nan, and 陳耀南. "The chlorophyll fluorescence and leaf reflectance spectra characteristics among different ecophysiological behavior plants." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/53928819798218020759.
Повний текст джерела國立中興大學
生命科學系
92
In order to understand the chlorophyll fluorescence and leaf reflectance spectral characteristics among species, 12 species with different elevation distribution and temperature adaptation were used. The experiments were made in the campus of National Chung Hsing University (78 m), Hui-Sun Forest Station (800 m), and Tatachia area (2600 m). The results indicated that the PSII efficiency estimated from chlorophyll fluorescence parameters of Pinus taiwanensis (conifer) was lesser influenced by the low temperature and high illumination than those of 2 Miscanthus (C4) species when they were measured in Tatachia. Among 2 Miscanthus species, low elevation origin M. floridulus was more influenced by low temperature than that of high elevation origin M. transomrrisonensis. In Tatachia, transplanted M. floridulus showed lower photochemical reflectance index (PRI) calculated from leaf reflectance spectra in the winter, indicating it required higher xanthophyll cycle to dissipate more excess absorbed energy due to PSII efficiency were more inhibited by low temperature. It also found that no difference of potential of PSII efficiency (Fv/Fm) between flatland and crest line grew P. taiwanensis in Hui-Sun Forest Station. However the PRI of crest line grew P. taiwanensis was lower than that of flatland grown in dry season, probably due to the difference of water condition between 2 habitats. It showed positive correlation between photosynthesis capacity (Pn) and electron transport rate (ETR) for C4 species. This regression coefficient was higher in the species with higher photosynthetic capacity, and no significant correlation could be found in Miscanthus, which showed the lowest Pn among 5 tested C4 species. When merged together of 5 C4 species to statistic analysis, the leaf with higher photosynthetic capacity showed higher portion of absorbed light energy for photochemical (P), and low portion for non-photochemical (D) dissipations. The slope between Pn and P, as well as Pn and D were decreasing with PAR increased. However, the portion of excess energy was not influenced by PAR. From November to December, which daily minimum temperature ranging from 11.6oC to 22.4oC, predawn Fv/Fm of mango (Mangifera indica, cv. Aiwen) and Podocarpus nagi decrease with low temperature, and mango was more influenced than P. nagi. On the contrary, predawn Fv/Fm of Taiwan alder (Alnus formosana) was lesser influenced by temperature. Nevertheless, predawn Fv/Fm showed a strong significant correlation with predawn PRI (PRIp) for statistical analysis when merged together of 3 species. Therefore PRIp could be used as an indicator to estimate the seasonal variation of the potential photochemical efficiency of PSII. Both Fv/Fm and Ф (actual PSII efficiency) showed significant curvilinear correlation with PRI (PRIn) when 3 species were merged together for statistical analysis which data measured at noon. However, more strong correlation between Fv/Fm and ΔPRI (PRIp - PRIn) as well as between Ф and ΔPRI were found. In addition, non photochemical quenching (NPQ) did not correlated with PRIn, but significant correlated with ΔPRI. Thus ΔPRI is suit to indicate the actual dissipation of the excess energy as well as PSII efficiency during illumination. As a conclusion, both chlorophyll fluorescence parameters and leaf reflectance spectra indexes are powerful tools for ecophysiological study.
Pereira, Maria de Lamares da Piedade e. Teixeira. "Monitoring the impact of soil management on plant spectral reflectance and soil-borne disease resistance." Master's thesis, 2016. http://hdl.handle.net/10348/6801.
Повний текст джерелаO solo e a sua biodiversidade são o motor de todos os sistemas de produção terrestres e serviços de ecossistemas. A mudança na produção agrícola de extensiva para intensiva tem um efeito negativo profundo nos solos e na sua biodiversidade. A perda de biodiversidade resulta em comunidades do solo menos complexas. Com o aumento da procura dos serviços dos ecossistemas, como o solo, existe a necessidade da melhoria da gestão dos solos e das produções agrícolas. Esta melhoria pode trazer vantagens a nível do desenvolvimento de práticas mais sustentáveis que contribuem para um desenvolvimento económico sustentável. A gestão sustentável de qualquer ecossistema requer, entre outras informações, uma compreensão completa da interação solo-planta para tentar descrever padrões naturais. As plantas produzem substância orgânica através da fotossíntese. A fotosíntese depende da absorção de luz pelos pigmentos fotossintéticos presentes na folha. Portanto, as propriedades óticas da folha são influenciadas pela concentração dos pigmentos fotossintéticos e metabolitos, do seu teor em água e da estrutura e anatomia da folha. A reflectância hiperspectral tem ganho importância comercial e científica, contudo, permanece ainda subdesenvolvida apesar do seu potencial. A deteção remota da vegetação é uma boa ferramenta, pois pode extrapolar escalas de tempo, e é cada vez mais utilizada para perceber interações planta-solo. Sabe-se que o sinal de reflectância é sensível a mudanças abióticas e bióticas, mas ainda há um longo caminho a percorrer. Consequentemente, foi realizado durante nove semanas um bioensaio com duas culturas diferentes, a Beterraba (Beta vulgaris) e o Milho (Zea mays). Foram plantadas em três diferentes tipos de gestão do solo e aplicados seis tipos de tratamentos. Os tratamentos aplicados foram: o fungo Rhizoctonia solani, o nemátode Pratylenchus penetrans, a radiação gama, os nutrientes, e o fungo R. solani com nutrientes e um controlo. Os tipos de gestão que foram aplicados foram o solo Biológico, os fertilizantes artificiais e Fertelizantes orgânicos. No total foram consideradas 3650 plantas. Os dados da refletância espetral foram obtidos com um espectrómetro de campo ASD plant-probe e clip-foliar. Um objetivo deste estudo consistiu em monitorizar a refletância espetral das folhas das duas espécies durante o período experimental. Os dados espetrais foram analisados utilizando índices de vegetação. Os efeitos do biota do solo foram analizados numa análise multivariada ANOVA com os fatores, espécie de planta, tipos de solo e tratamentos. A biomassa total de patogénicos tende a aumentar quanto mais intensiva for a prática agricola. No solo Biológico observou-se uma intensificação da cor verde da planta nas duas espécies, com o aumento da disponibilidade de nutrientes. A adição de fertelizante pode ter influenciado a resistência das plantas às doenças do solo. A menor biomassa foi encontrada no tratamento com radiação gama (estéril), sugerindo que o biota do solo influenciou o desempenho da planta. O melhor tipo de gestão do solo teve um efeito positivo no crescimento das plantas. As melhores práticas agricolas permitem uma supressão das doenças inoculadas. Foi demonstrado que o espetro da planta difere quando é induzido stresse e também consoante o tipo de gestão do solo. A melhor gestão agrícola foi considerada a Biológica.
Soil and soil biodiversity are the driving force of all the terrestrial production systems and ecosystem services. The intensification of agriculture production and shifts from extensive crop rotation have, regularly, a profound negative effect on soils and their biodiversity. Biodiversity losses result in less complex soil communities. The increasing demand of soil ecosystem implies the improvement of soil and crop management, and it’s a key opportunity for supporting sustainable economic development. The sustainable management of any ecosystem requires, amongst other information, a thorough understanding of plant-soil feedback attempting to describe natural patterns and relations between the plants and their environment. Plants produce organic substances by photosynthesis. Photosynthesis depends upon the absorption of light by pigments, as chlorophyll-a among other accessory pigments, in the leaves of the plants. Therefore, leaf optical properties are influenced by the concentration of the photosynthetic pigments, metabolites, water content, leaf structure and leaf anatomy. Hyperspectral reflectance in remote sensing has gained scientific and commercial importance but still remains underdeveloped despite its potential. Vegetation remote sensing is a great tool, as it can extrapolate to synoptic scales and time sequences can be acquired. It is increasingly used for measurements of agricultural crop condition and also for plant-soil interactions. It is known that reflectance signal is sensitive to abiotic changes, but concerning biotic changes, there are still several limitations. Therefore, was conducted a 9 weeks greenhouse bioassay with two different crops, Sugar beet (Beta vulgaris) and Corn (Zea mays), three different types of soil management and six different treatments were applied. The treatments applied were: the fungus Rhizoctonia solani, the nematode Pratylenchus penetrans, the Gamma radiation, the nutrients, the R. solani with nutrients and a control treatment. The types of management were the Biologic, the Artificial Fertilizer and the Manure. In total were 3650 plants. Spectral reflectance data were collected with an ASD Fieldspec 3 spectrometer with an ASD plant-probe and leaf-clip device attached. One of the objectives of the measurement was to monitor the differences between leaf reflectance over time. The spectral data was analyzed using vegetation indices. The effects of soil biota were analyzed in a multivariate ANOVA analysis with plant species, soil regime and soil treatment. The total biomass of the pathogens increase with a more intensive agriculture and shoot biomass in both plant species increased with disposal of the nutrient supply in the Biologic soil. The application of manure compost that is rich in nitrogen may have reduced soil-borne diseases. The lowest biomass was found in the sterilized treatments suggesting that the soil biota has influenced the plant performance. The best soil management had positive effect in growth of the plants. Disease suppression can be influenced by management practices. It was demonstrated that plant spectral signatures changes due induced stress and soil type. The best soil regime overall in this study case was considered the Biologic type.
Prasad, Bishwajit. "The potential for using canopy spectral reflectance as an indirect selection tool for yield improvement in winter wheat." 2006. http://digital.library.okstate.edu/etd/umi-okstate-1915.pdf.
Повний текст джерелаHwang, Mon-Yuan, and 黃盟元. "Studies on reflectance spectra of plants leaves and the related physiological characters in different ecological habitats and seasons." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/80327596581588152296.
Повний текст джерелаAxness, Daniel S. "Estimating ground cover via spectral data." Thesis, 1991. http://hdl.handle.net/1957/36337.
Повний текст джерелаGraduation date: 1992
Книги з теми "Plant reflectance spectra"
Ripple, William J. Spectral reflectance relationships to leaf water stress. Corvallis, Or: Environmental Remote Sensing Applications Laboratory - ERSAL, Oregon State University, 1986.
Знайти повний текст джерелаM, Ager C., Power M. S, and Geological Survey (U.S.), eds. Spectral reflectance changes in greenhouse-grown metal-doped plants. [Denver, Colo.?]: Dept. of the Interior, U.S. Geological Survey, 1988.
Знайти повний текст джерелаAxness, Daniel S. Estimating ground cover via spectral data. 1991.
Знайти повний текст джерелаChance, Kelly, and Randall V. Martin. Basic Solar and Planetary Properties. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0001.
Повний текст джерелаЧастини книг з теми "Plant reflectance spectra"
Meireles, José Eduardo, Brian O’Meara, and Jeannine Cavender-Bares. "Linking Leaf Spectra to the Plant Tree of Life." In Remote Sensing of Plant Biodiversity, 155–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_7.
Повний текст джерелаUstin, Susan L., and Stéphane Jacquemoud. "How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality." In Remote Sensing of Plant Biodiversity, 349–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_14.
Повний текст джерелаKlančnik, Katja, Igor Zelnik, Primož Gnezda, and Alenka Gaberščik. "Do Reflectance Spectra of Different Plant Stands in Wetland Indicate Species Properties?" In The Role of Natural and Constructed Wetlands in Nutrient Cycling and Retention on the Landscape, 73–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08177-9_6.
Повний текст джерелаCarter, Gregory A., and Lee Estep. "General Spectral Characteristics of Leaf Reflectance Responses to Plant Stress and Their Manifestation at the Landscape Scale." In From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems, 271–93. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-1620-8_12.
Повний текст джерелаHummel, John W., and Jing Yu. "Spectral Reflectance Pattern Recognition for Segmenting Corn Plants and Weeds." In Proceedings of the Fourth International Conference on Precision Agriculture, 1523–36. Madison, WI, USA: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 2015. http://dx.doi.org/10.2134/1999.precisionagproc4.c54b.
Повний текст джерелаKong, Weiping, Yinli Bi, Wenjiang Huang, Lingli Tang, Chuanrong Li, and Lingling Ma. "Nondestructive Evaluation of Inoculation Effects of AMF and Bradyrhizobium japonicum on Soybean under Drought Stress From Reflectance Spectroscopy." In Soybean for Human Consumption and Animal Feed. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88673.
Повний текст джерелаEguchi, Hiromi. "DIGITAL PROCESSING OF PLANT IMAGES SELECTED BY SPECTRAL CHARACTERISTICS OF REFLECTANCE FOR EVALUATION OF GROWTH." In Measurement Techniques in Plant Science, 361–72. Elsevier, 1990. http://dx.doi.org/10.1016/b978-0-12-330585-5.50024-4.
Повний текст джерелаKapp Jr., Claudio, Eduardo Fávero Caires, and Alaine Margarete Guimarães. "Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using ANN Algorithms." In Innovations and Trends in Environmental and Agricultural Informatics, 156–72. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5978-8.ch007.
Повний текст джерелаParra, Javier Lo, Jacinto Garrido Velarde, Jesus Barrena González, and Manuel Pulido Fernández. "Ecohydrological Behavior of Semiarid Ecosystems of Chile in Present and Future Climate Scenarios." In Practice, Progress, and Proficiency in Sustainability, 60–74. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7391-4.ch005.
Повний текст джерелаRango, Albert, and Jerry Ritchie. "Applications of Remotely Sensed Data from the Jornada Basin." In Structure and Function of a Chihuahuan Desert Ecosystem. Oxford University Press, 2006. http://dx.doi.org/10.1093/oso/9780195117769.003.0019.
Повний текст джерелаТези доповідей конференцій з теми "Plant reflectance spectra"
Xu, Tingyan, Taotao Zhu, Ting Yang, Yanxin Guo, Jingqi Xu, Wandong Chang, Siyi Fang, and Kangkang Zhu. "Research advances in reflectance spectra of plant leafs." In Fourth Seminar on Novel Optoelectronic Detection Technology and Application, edited by Weiqi Jin and Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2314999.
Повний текст джерелаKARLOVSKA, Amanda, Inga GRĪNFELDE, Ina ALSIŅA, Gints PRIEDĪTIS, and Daina ROZE. "PLANT REFLECTED SPECTRA DEPENDING ON BIOLOGICAL CHARACTERISTICS AND GROWTH CONDITIONS." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.045.
Повний текст джерелаShi, Runhe, Huifang Zhang, Juan Sun, Wei Gao, Dafang Zhuang, and Zheng Niu. "Responses of plant biochemical substances to reflectance spectra at leaf and canopy scales." In Optical Engineering + Applications, edited by Wei Gao and Hao Wang. SPIE, 2008. http://dx.doi.org/10.1117/12.794088.
Повний текст джерелаShahrimie, M. A. Mohd, Puneet Mishra, Stien Mertens, Stijn Dhondt, Nathalie Wuyts, and Paul Scheunders. "Modeling effects of illumination and plant geometry on leaf reflectance spectra in close-range hyperspectral imaging." In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016. http://dx.doi.org/10.1109/whispers.2016.8071753.
Повний текст джерелаSahba, Kaveh, Sreten Askraba, and Kamal E. Alameh. "Photonics-based Spectral Reflectance Sensor for Plant Discrimination." In 2007 the Joint International Conference on Optical Internet (COIN) and Australian Conference on Optical Fibre Technology (ACOFT). IEEE, 2007. http://dx.doi.org/10.1109/coinacoft.2007.4519146.
Повний текст джерелаSahba, Kaveh, Sreten Askraba, and Kamal E. Alameh. "Photonics-based Spectral Reflectance Sensor for Plant Discrimination." In 2006 Australian Conference on Optical Fibre technology (ACOFT). IEEE, 2007. http://dx.doi.org/10.1109/acoft.2007.4516239.
Повний текст джерелаMakino, Toshiro, and Hidenobu Wakabayashi. "Experimental Verification of Kirchhoff’s Thermal Radiation Law on Surfaces With Emittance Spectra Characterized by Optical Interference Phenomena." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22718.
Повний текст джерелаBao, Hua, and Xiulin Ruan. "Radiative Properties of GaAs From First Principles Calculations." In ASME 2008 Heat Transfer Summer Conference collocated with the Fluids Engineering, Energy Sustainability, and 3rd Energy Nanotechnology Conferences. ASMEDC, 2008. http://dx.doi.org/10.1115/ht2008-56341.
Повний текст джерелаAkbarzadeh, Saman, Selam Ahderom, and Kamal Alameh. "Application of spectral reflectance for increasing plant discrimination speed in precision agriculture." In 2019 IEEE 16th International Conference on Smart Cities: Improving Quality of Life Using ICT & IoT and AI (HONET-ICT). IEEE, 2019. http://dx.doi.org/10.1109/honet.2019.8907994.
Повний текст джерелаVelichkova, Kalinka, and Dora Krezhova. "SENSITIVITY OF REMOTELY-SENSED SPECTRAL REFLECTANCE TO BIOPHYSICAL VARIABLES OF PLANTS." In RAD Conference. RAD Association, 2017. http://dx.doi.org/10.21175/radproc.2017.56.
Повний текст джерелаЗвіти організацій з теми "Plant reflectance spectra"
Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.
Повний текст джерелаAgassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.
Повний текст джерелаBonfil, David J., Daniel S. Long, and Yafit Cohen. Remote Sensing of Crop Physiological Parameters for Improved Nitrogen Management in Semi-Arid Wheat Production Systems. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7696531.bard.
Повний текст джерела