Plant functional remote sensing and smart farming applications
Keywords:
remote sensing, smart farming, smart greenhouse, IoT, ICT, image analysisAbstract
Plants have the distinctive 3D spatial structure that varies among organs, species and communities, and the spatial structure changes as they interact with their environments. The functions linked to fundamental biological activities such as transpiration, photosynthesis, and growth are also affected by the spatial structure and the environment. In order to promote smart farming using information and communication technology (ICT), it is necessary to measure and utilize information at the cell-organ of plants to the individual and the community levels and the environments in two or even three dimensions. Therefore, this paper introduced the outline of remote sensing of plant functioning and examples of the 3D remote sensing from relatively short distances using drones and ground Lidar. The quality control of rice in the paddy field and chlorophyll fluorescence imaging for photosynthetic diagnosis were also introduced. In addition, a field smart farm and a smart greenhouse, which heavily utilize ICT, built at Takasaki University of Health and Welfare in Gunma, Japan, were also introduced. Keywords: remote sensing, smart farming, smart greenhouse, IoT, ICT, image analysis DOI: 10.25165/j.ijabe.20221504.7375 Citation: Omasa K, Ono E, Ishigami Y, Shimizu Y, Araki Y. Plant functional remote sensing and smart farming applications. Int J Agric & Biol Eng, 2022; 15(4): 1–6.References
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[7] Omasa K, Qiu G Y, Watanuki K, Yoshimi K, Akiyama Y. Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees. Environ. Sci. Technol., 2003; 37: 1198–1201.
[8] Omasa K, Saji H, Youssefian S, Kondo N. (Ed.) Air pollution and plant biotechnology -Prospects for phytomonitoring and phytoremediation. Tokyo: Springer-Verlag, 2002; 455p.
[9] Omasa K. Image sensing and phytobiological information. In: Munack A (Ed.) CIGR handbook of agricultural engineering. IV Information technology, St. Joseph: ASABE, 2006; pp.217–244.
[10] Konishi A, Eguchi A, Hosoi F, Omasa K. 3D monitoring spatio-temporal effects of herbicide on a whole plant using combined range and chlorophyll a fluorescence imaging. Funct. Plant Biol., 2009; 36: 874–879.
[11] Omasa K, Konishi A, Tamura H, Hosoi F. 3D confocal laser scanning microscopy for the analysis of chlorophyll fluorescence parameters of chloroplasts in intact leaf tissues. Plant Cell Physiol., 2009; 50: 90–105.
[12] Matsuda M, Hosaka Y, Omasa K. Quality assessment of grains using functional remote sensing. Iden(Genetics), 2010; 64(2): 81–86. (in Japanse)
[13] Hosoi F, Nakabayashi K, Omasa K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors, 2011; 11: 2166–2174.
[14] Hosoi F, Omasa K. Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS J. Photogram. Remote Sens., 2012; 74: 11–19.
[15] Omasa K. Remote sensing of plant functioning and the development for phenomics researches. Trends in the Sciences, 2016; 21(2): 72–76. (in Japanese)
[16] Teng P, Zhang Y, Shimizu Y, Hosoi F, Omasa K. Accuracy assessment in 3D remote sensing of rice plants in paddy field using a small UAV. Eco-Eng., 2016; 28: 107–112.
[17] Teng P, Ono E, Zhang Y, Aono M, Shimizu Y, Hosoi F, et al. Estimation of ground surface and accuracy assessments of growth parameters for a sweet potato community in ridge cultivation. Remote Sens., 2019; 11(1487): 1–18.
[18] Omasa K. The importance of encounters. Climate in Biosp., 2018; 18: 29–38. (in Japanese)
[19] Omasa K. Publication lists before 2017. http://park.itc.u-tokyo.ac.jp/ joho/Omasa/papers2010311.html, http://park.itc.u-tokyo.ac.jp/joho/ Omasa/books20090123.html. Accessed on [2021-12-30].
[20] Omasa K. Publication lists after 2000. https://researchmap.jp/ read0123279. Accessed on [2021-12-30].
[2] Omasa K, Hosoi F, Konishi A. 3D lidar imaging for detecting and understanding plant responses and canopy structure. J. Exp. Bot., 2007; 58: 881–898.
[3] Jones H G. Plants and microclimate. 3rd ed. Cambridge: Cambridge University Press, 2014; 423 p.
[4] Jacquemond S, Ustin S. Leaf optical properties. Cambridge: Cambridge University Press, 2019; 566p.
[5] Omasa K, Croxdale J G. Image analysis of stomatal movements and gas exchange. In: Häder D-P (Ed.) Image analysis in biology. Boca Raton: CRC Press, 1991; pp.171–193.
[6] Omasa K. 3-D color video microscopy of intact plants. In: Häder D-P (Ed.), Image analysis: methods and applications, 2nd ed. Boca Raton: CRC Press, 2000; pp.257–273.
[7] Omasa K, Qiu G Y, Watanuki K, Yoshimi K, Akiyama Y. Accurate estimation of forest carbon stocks by 3-D remote sensing of individual trees. Environ. Sci. Technol., 2003; 37: 1198–1201.
[8] Omasa K, Saji H, Youssefian S, Kondo N. (Ed.) Air pollution and plant biotechnology -Prospects for phytomonitoring and phytoremediation. Tokyo: Springer-Verlag, 2002; 455p.
[9] Omasa K. Image sensing and phytobiological information. In: Munack A (Ed.) CIGR handbook of agricultural engineering. IV Information technology, St. Joseph: ASABE, 2006; pp.217–244.
[10] Konishi A, Eguchi A, Hosoi F, Omasa K. 3D monitoring spatio-temporal effects of herbicide on a whole plant using combined range and chlorophyll a fluorescence imaging. Funct. Plant Biol., 2009; 36: 874–879.
[11] Omasa K, Konishi A, Tamura H, Hosoi F. 3D confocal laser scanning microscopy for the analysis of chlorophyll fluorescence parameters of chloroplasts in intact leaf tissues. Plant Cell Physiol., 2009; 50: 90–105.
[12] Matsuda M, Hosaka Y, Omasa K. Quality assessment of grains using functional remote sensing. Iden(Genetics), 2010; 64(2): 81–86. (in Japanse)
[13] Hosoi F, Nakabayashi K, Omasa K. 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors, 2011; 11: 2166–2174.
[14] Hosoi F, Omasa K. Estimation of vertical plant area density profiles in a rice canopy at different growth stages by high-resolution portable scanning lidar with a lightweight mirror. ISPRS J. Photogram. Remote Sens., 2012; 74: 11–19.
[15] Omasa K. Remote sensing of plant functioning and the development for phenomics researches. Trends in the Sciences, 2016; 21(2): 72–76. (in Japanese)
[16] Teng P, Zhang Y, Shimizu Y, Hosoi F, Omasa K. Accuracy assessment in 3D remote sensing of rice plants in paddy field using a small UAV. Eco-Eng., 2016; 28: 107–112.
[17] Teng P, Ono E, Zhang Y, Aono M, Shimizu Y, Hosoi F, et al. Estimation of ground surface and accuracy assessments of growth parameters for a sweet potato community in ridge cultivation. Remote Sens., 2019; 11(1487): 1–18.
[18] Omasa K. The importance of encounters. Climate in Biosp., 2018; 18: 29–38. (in Japanese)
[19] Omasa K. Publication lists before 2017. http://park.itc.u-tokyo.ac.jp/ joho/Omasa/papers2010311.html, http://park.itc.u-tokyo.ac.jp/joho/ Omasa/books20090123.html. Accessed on [2021-12-30].
[20] Omasa K. Publication lists after 2000. https://researchmap.jp/ read0123279. Accessed on [2021-12-30].
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Published
2022-09-04
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Omasa, K., Ono, E., Ishigami, Y., Shimizu, Y., & Araki, Y. (2022). Plant functional remote sensing and smart farming applications. International Journal of Agricultural and Biological Engineering, 15(4), 1–6. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7375
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