Skip to main content

2022 | OriginalPaper | Buchkapitel

Ground-Penetrating Radar-Mounted Drones in Agriculture

verfasst von : Petri Linna, Antti Halla, Nathaniel Narra

Erschienen in: New Developments and Environmental Applications of Drones

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

For precision farming, we need more and more accurate information not only about the crop, but also the soil. Surface measurement is fairly easy, with huge amounts of data being received from satellites all the time. With the help of drones, that data can still be refined, but the measurement price increases depending on the equipment as well as working time. With regard to soil measurement, measurement slows down and becomes more expensive.
The study mapped research papers of ground-penetrating radar and those different topics where they have been used. The topics were limited to agriculture only. The used frequencies were discovered from every topic. The study investigated artificial intelligence papers related to ground-penetrating radar and needs to begin an own artificial intelligence study in this subject. Finally, various concepts were evaluated for conducting ground-penetrating radar research. One of these concepts was to connect a ground-penetrating radar to a drone.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Akinsunmade, A., Tomecka-Suchoń, S., & Pysz, P. (2019). Correlation between agrotechnical properties of selected soil types and corresponding GPR response. Acta Geophysica, 67(6), 1913–1919.CrossRef Akinsunmade, A., Tomecka-Suchoń, S., & Pysz, P. (2019). Correlation between agrotechnical properties of selected soil types and corresponding GPR response. Acta Geophysica, 67(6), 1913–1919.CrossRef
2.
Zurück zum Zitat Algeo, J., Slater, L., Binley, A., Van Dam, R. L., & Watts, C. (2018). A comparison of ground-penetrating radar early-time signal approaches for mapping changes in shallow soil water content. Vadose Zone Journal, 17(1), 180001.CrossRef Algeo, J., Slater, L., Binley, A., Van Dam, R. L., & Watts, C. (2018). A comparison of ground-penetrating radar early-time signal approaches for mapping changes in shallow soil water content. Vadose Zone Journal, 17(1), 180001.CrossRef
3.
Zurück zum Zitat Allred, B., Wishart, D., Martinez, L., Schomberg, H., Mirsky, S., Meyers, G., Elliott, J., & Charyton, C. (2018). Delineation of agricultural drainage pipe patterns using ground penetrating radar integrated with a real-time kinematic global navigation satellite system. Agriculture (Switzerland), 8(11), 167. Allred, B., Wishart, D., Martinez, L., Schomberg, H., Mirsky, S., Meyers, G., Elliott, J., & Charyton, C. (2018). Delineation of agricultural drainage pipe patterns using ground penetrating radar integrated with a real-time kinematic global navigation satellite system. Agriculture (Switzerland), 8(11), 167.
4.
Zurück zum Zitat Allred, B. J. (2013). A GPR agricultural drainage pipe detection case study: Effects of antenna orientation relative to drainage pipe directional trend. Journal of Environmental and Engineering Geophysics, 18(1), 55–69.CrossRef Allred, B. J. (2013). A GPR agricultural drainage pipe detection case study: Effects of antenna orientation relative to drainage pipe directional trend. Journal of Environmental and Engineering Geophysics, 18(1), 55–69.CrossRef
5.
Zurück zum Zitat Alvarez, J. K., & Kodagoda, S. (2018). Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 (pp. 611–616). Alvarez, J. K., & Kodagoda, S. (2018). Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring. In Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018 (pp. 611–616).
6.
Zurück zum Zitat Awak, E., George, A., Urang, J., & Udoh, J. (2017). Determination of soil electrical conductivity using ground penetrating radar (GPR) for precision agriculture. International Journal of Scientific & Engineering Research, 8(1). Awak, E., George, A., Urang, J., & Udoh, J. (2017). Determination of soil electrical conductivity using ground penetrating radar (GPR) for precision agriculture. International Journal of Scientific & Engineering Research, 8(1).
7.
Zurück zum Zitat Behari, J. (2005a). Dielectric Constant of Soil (pp. 92–106). Dordrecht: Springer. Behari, J. (2005a). Dielectric Constant of Soil (pp. 92–106). Dordrecht: Springer.
8.
Zurück zum Zitat Behari, J. (2005b). Soil Moisture Models (pp. 107–124). Dordrecht: Springer. Behari, J. (2005b). Soil Moisture Models (pp. 107–124). Dordrecht: Springer.
9.
Zurück zum Zitat Benedetto, A. (2010). Water content evaluation in unsaturated soil using GPR signal analysis in the frequency domain. Journal of Applied Geophysics, 71(1), 26–35.CrossRef Benedetto, A. (2010). Water content evaluation in unsaturated soil using GPR signal analysis in the frequency domain. Journal of Applied Geophysics, 71(1), 26–35.CrossRef
10.
Zurück zum Zitat Benedetto, F., & Tosti, F. (2013). GPR spectral analysis for clay content evaluation by the frequency shift method. Journal of Applied Geophysics, 97, 89–96.CrossRef Benedetto, F., & Tosti, F. (2013). GPR spectral analysis for clay content evaluation by the frequency shift method. Journal of Applied Geophysics, 97, 89–96.CrossRef
11.
Zurück zum Zitat Caorsi, S., & Stasolla, M. (2009). A Machine Learning Algorithm for GPR Sub-surface Prospection. In 2009 Mediterrannean Microwave Symposium (MMS). Caorsi, S., & Stasolla, M. (2009). A Machine Learning Algorithm for GPR Sub-surface Prospection. In 2009 Mediterrannean Microwave Symposium (MMS).
12.
Zurück zum Zitat Cerquera, M. R. P., Montaño, J. D. C., & Mondragón, I. (2017). UAV for landmine detection using SDR-based GPR technology. In Robots Operating in Hazardous Environments. IntechOpen. Cerquera, M. R. P., Montaño, J. D. C., & Mondragón, I. (2017). UAV for landmine detection using SDR-based GPR technology. In Robots Operating in Hazardous Environments. IntechOpen.
13.
Zurück zum Zitat Chantasen, N., Boonpoonga, A., Athikulwongse, K., Kaemarungsi, K., & Akkaraekthalin, P. (2020). Mapping the physical and dielectric properties of layered soil using short-time matrix pencil method-based ground-penetrating radar. IEEE Access, 8, 105610–105621.CrossRef Chantasen, N., Boonpoonga, A., Athikulwongse, K., Kaemarungsi, K., & Akkaraekthalin, P. (2020). Mapping the physical and dielectric properties of layered soil using short-time matrix pencil method-based ground-penetrating radar. IEEE Access, 8, 105610–105621.CrossRef
14.
Zurück zum Zitat Chen, X. L., Tian, M., & Yao, W. B. (2005). GPR signals de-noising by using wavelet networks. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 4690–4693). Chen, X. L., Tian, M., & Yao, W. B. (2005). GPR signals de-noising by using wavelet networks. In 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 (pp. 4690–4693).
15.
Zurück zum Zitat Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture. Agronomy Journal, 95(3), 455–471. Corwin, D. L., & Lesch, S. M. (2003). Application of soil electrical conductivity to precision agriculture. Agronomy Journal, 95(3), 455–471.
16.
Zurück zum Zitat Daliakopoulos, I. N., Tsanis, I. K., Koutroulis, A., Kourgialas, N. N., Varouchakis, A. E., Karatzas, G. P., & Ritsema, C. J. (2016). The threat of soil salinity: A European scale review. Science of The Total Environment, 573, 727–739CrossRef Daliakopoulos, I. N., Tsanis, I. K., Koutroulis, A., Kourgialas, N. N., Varouchakis, A. E., Karatzas, G. P., & Ritsema, C. J. (2016). The threat of soil salinity: A European scale review. Science of The Total Environment, 573, 727–739CrossRef
17.
Zurück zum Zitat Doolittle, J. A., Jenkinson, B., Hopkins, D., Ulmer, M., & Tuttle, W. (2006). Hydropedological investigations with ground-penetrating radar (GPR): Estimating water-table depths and local ground-water flow pattern in areas of coarse-textured soils. In Geoderma, vol. 131 (pp. 317–329). Amsterdam: Elsevier. Doolittle, J. A., Jenkinson, B., Hopkins, D., Ulmer, M., & Tuttle, W. (2006). Hydropedological investigations with ground-penetrating radar (GPR): Estimating water-table depths and local ground-water flow pattern in areas of coarse-textured soils. In Geoderma, vol. 131 (pp. 317–329). Amsterdam: Elsevier.
18.
Zurück zum Zitat Economou, N., Vafidis, A., Benedetto, F., & Alani, A. M. (2015). GPR data processing techniques. In A. Benedetto & L. Pajewski (Eds.), Civil Engineering Applications of Ground Penetrating Radar (pp. 281–297). Cham: Springer. Economou, N., Vafidis, A., Benedetto, F., & Alani, A. M. (2015). GPR data processing techniques. In A. Benedetto & L. Pajewski (Eds.), Civil Engineering Applications of Ground Penetrating Radar (pp. 281–297). Cham: Springer.
19.
Zurück zum Zitat Elsaadouny, M., Barowski, J., & Rolfes, I. (2019). The subsurface objects classification using a convolutional neural network. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2019 (pp. 874–877). Elsaadouny, M., Barowski, J., & Rolfes, I. (2019). The subsurface objects classification using a convolutional neural network. In 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2019 (pp. 874–877).
21.
Zurück zum Zitat García-Fernández, M., Álvarez López, Y., De Mitri, A., Castrillo Martínez, D., Álvarez-Narciandi, G., & Las-Heras Andrés, F. (2020). Portable and easily-deployable air-launched GPR scanner. Remote Sensing, 12(11), 1833.CrossRef García-Fernández, M., Álvarez López, Y., De Mitri, A., Castrillo Martínez, D., Álvarez-Narciandi, G., & Las-Heras Andrés, F. (2020). Portable and easily-deployable air-launched GPR scanner. Remote Sensing, 12(11), 1833.CrossRef
22.
Zurück zum Zitat Giannakis, I., Giannopoulos, A., & Warren, C. (2019). A machine learning-based fast-forward solver for ground penetrating radar with application to full-waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4417–4426.CrossRef Giannakis, I., Giannopoulos, A., & Warren, C. (2019). A machine learning-based fast-forward solver for ground penetrating radar with application to full-waveform inversion. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4417–4426.CrossRef
23.
Zurück zum Zitat Giovanneschi, F., Mishra, K. V., Gonzalez-Huici, M. A., Eldar, Y. C., & Ender, J. H. G. (2019). Dictionary learning for adaptive GPR landmine classification. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 10036–10055.CrossRef Giovanneschi, F., Mishra, K. V., Gonzalez-Huici, M. A., Eldar, Y. C., & Ender, J. H. G. (2019). Dictionary learning for adaptive GPR landmine classification. IEEE Transactions on Geoscience and Remote Sensing, 57(12), 10036–10055.CrossRef
25.
Zurück zum Zitat Helmisaari, H.-S., Lehto, T., & Makkonen, K. (2000). Fine roots and soil properties. In E. Mälkönen (Ed.), Forest Condition in a Changing Environment: The Finnish Case (pp. 203–217). Dordrecht: Springer.CrossRef Helmisaari, H.-S., Lehto, T., & Makkonen, K. (2000). Fine roots and soil properties. In E. Mälkönen (Ed.), Forest Condition in a Changing Environment: The Finnish Case (pp. 203–217). Dordrecht: Springer.CrossRef
26.
Zurück zum Zitat Huisman, J., Hubbard, S., Redman, J., & Annan, P. (2003). Measuring soil water content with ground penetrating radar: A review. Vadose Zone Journal, 2, 476–491. Huisman, J., Hubbard, S., Redman, J., & Annan, P. (2003). Measuring soil water content with ground penetrating radar: A review. Vadose Zone Journal, 2, 476–491.
28.
Zurück zum Zitat Kaur, P., Dana, K. J., Romero, F. A., & Gucunski, N. (2016). Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Transactions on Cybernetics, 46(10), 2265–2276.CrossRef Kaur, P., Dana, K. J., Romero, F. A., & Gucunski, N. (2016). Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Transactions on Cybernetics, 46(10), 2265–2276.CrossRef
29.
Zurück zum Zitat Kim, N., Kim, S., An, Y. K., & Lee, J. J. (2019). Triplanar imaging of 3-D GPR data for deep-learning-based underground object detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11), 4446–4456.CrossRef Kim, N., Kim, S., An, Y. K., & Lee, J. J. (2019). Triplanar imaging of 3-D GPR data for deep-learning-based underground object detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11), 4446–4456.CrossRef
30.
Zurück zum Zitat Koyan, P., & Tronicke, J. (2020). 3d modeling of ground-penetrating radar data across a realistic sedimentary model. Computers & Geosciences, 137, 104422.CrossRef Koyan, P., & Tronicke, J. (2020). 3d modeling of ground-penetrating radar data across a realistic sedimentary model. Computers & Geosciences, 137, 104422.CrossRef
31.
Zurück zum Zitat Lameri, S., Lombardi, F., Bestagini, P., Lualdi, M., & Tubaro, S. (2017). Landmine detection from GPR data using convolutional neural networks. In 25th European Signal Processing Conference, EUSIPCO 2017 (vol. 2017, pp. 508–512). Lameri, S., Lombardi, F., Bestagini, P., Lualdi, M., & Tubaro, S. (2017). Landmine detection from GPR data using convolutional neural networks. In 25th European Signal Processing Conference, EUSIPCO 2017 (vol. 2017, pp. 508–512).
32.
Zurück zum Zitat Linna, P., Aaltonen, T., Halla, A., Grönman, J., & Narra, N. (2020). Conceptual design of an autonomous rover with ground penetrating radar: Application in characterizing soils using deep learning. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1134–1139) Linna, P., Aaltonen, T., Halla, A., Grönman, J., & Narra, N. (2020). Conceptual design of an autonomous rover with ground penetrating radar: Application in characterizing soils using deep learning. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1134–1139)
33.
Zurück zum Zitat Liu, X., Chen, J., Butnor, J. R., Qin, G., Cui, X., Fan, B., Lin, H., & Guo, L. (2020). Noninvasive 2D and 3D mapping of root zone soil moisture through the detection of coarse roots with ground-penetrating radar. Water Resources Research, 56(5), e2019WR026930. Liu, X., Chen, J., Butnor, J. R., Qin, G., Cui, X., Fan, B., Lin, H., & Guo, L. (2020). Noninvasive 2D and 3D mapping of root zone soil moisture through the detection of coarse roots with ground-penetrating radar. Water Resources Research, 56(5), e2019WR026930.
34.
Zurück zum Zitat Liu, X., Dong, X., & Leskovar, D. I. (2016). Ground penetrating radar for underground sensing in agriculture: A review. International Agrophysics, 30, 533–543.CrossRef Liu, X., Dong, X., & Leskovar, D. I. (2016). Ground penetrating radar for underground sensing in agriculture: A review. International Agrophysics, 30, 533–543.CrossRef
35.
Zurück zum Zitat Liu, X., Dong, X., Xue, Q., Leskovar, D. I., Jifon, J., Butnor, J. R., & Marek, T. (2018). Ground penetrating radar (GPR) detects fine roots of agricultural crops in the field. Plant Soil, 423, 517–531.CrossRef Liu, X., Dong, X., Xue, Q., Leskovar, D. I., Jifon, J., Butnor, J. R., & Marek, T. (2018). Ground penetrating radar (GPR) detects fine roots of agricultural crops in the field. Plant Soil, 423, 517–531.CrossRef
38.
Zurück zum Zitat Nevavuori, P., Narra, N., & Lipping, T. (2019). Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163, 104859.CrossRef Nevavuori, P., Narra, N., & Lipping, T. (2019). Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163, 104859.CrossRef
39.
Zurück zum Zitat Pham, M., & Lefèvre, S. (2018). Buried object detection from B-scan ground penetrating radar data using faster-RCNN. In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 6804–6807). Pham, M., & Lefèvre, S. (2018). Buried object detection from B-scan ground penetrating radar data using faster-RCNN. In IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 6804–6807).
40.
Zurück zum Zitat Picetti, F., Testa, G., Lombardi, F., Bestagini, P., Lualdi, M., & Tubaro, S. (2018). Convolutional autoencoder for landmine detection on GPR Scans. In 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018. Piscataway: Institute of Electrical and Electronics Engineers Inc. Picetti, F., Testa, G., Lombardi, F., Bestagini, P., Lualdi, M., & Tubaro, S. (2018). Convolutional autoencoder for landmine detection on GPR Scans. In 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018. Piscataway: Institute of Electrical and Electronics Engineers Inc.
41.
Zurück zum Zitat Reichman, D., Collins, L. M., & Malof, J. M. (2017). Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar. In 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc. Reichman, D., Collins, L. M., & Malof, J. M. (2017). Some good practices for applying convolutional neural networks to buried threat detection in Ground Penetrating Radar. In 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers Inc.
42.
Zurück zum Zitat Ristic, A., Petrovacki, D., & Vrtunski, M. (2014). Ground penetrating radar technology - the usage in agriculture. Research Journal of Agricultural Science, 46, 53–58. ISSN: 2066-1843 Ristic, A., Petrovacki, D., & Vrtunski, M. (2014). Ground penetrating radar technology - the usage in agriculture. Research Journal of Agricultural Science, 46, 53–58. ISSN: 2066-1843
43.
Zurück zum Zitat Seyfried, D., Busche, A., Janning, R., Schmidt-Thieme, L., & Schoebel, J. (2012). Information extraction from ultrawideband ground penetrating radar data: A machine learning approach. In 2012 the 7th German Microwave Conference, GeMiC 2012. Seyfried, D., Busche, A., Janning, R., Schmidt-Thieme, L., & Schoebel, J. (2012). Information extraction from ultrawideband ground penetrating radar data: A machine learning approach. In 2012 the 7th German Microwave Conference, GeMiC 2012.
44.
Zurück zum Zitat Shen, X., Foster, T., Baldi, H., Dobreva, I., Burson, B., Hays, D., Tabien, R., & Jessup, R. (2019). Quantification of soil organic carbon in biochar-amended soil using ground penetrating radar (GPR). Remote Sensing, 11(23), 1–12. https://doi.org/10.3390/rs11232874 Shen, X., Foster, T., Baldi, H., Dobreva, I., Burson, B., Hays, D., Tabien, R., & Jessup, R. (2019). Quantification of soil organic carbon in biochar-amended soil using ground penetrating radar (GPR). Remote Sensing, 11(23), 1–12. https://​doi.​org/​10.​3390/​rs11232874
45.
Zurück zum Zitat Sonoda, J., & Kimoto, T. (2019). Object identification form GPR images by deep learning. In Asia-Pacific Microwave Conference Proceedings, APMC (vol. 2018, pp. 1298–1300). Sonoda, J., & Kimoto, T. (2019). Object identification form GPR images by deep learning. In Asia-Pacific Microwave Conference Proceedings, APMC (vol. 2018, pp. 1298–1300).
47.
Zurück zum Zitat Todkar, S. S., Le Bastard, C., Ihamouten, A., Baltazart, V., Dérobert, X., Fauchard, C., Guilbert, D., & Bosc, F. (2017). Detection of debondings with ground penetrating radar using a machine learning method. In 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings (vol. 2017). Institute of Electrical and Electronics Engineers Inc. Todkar, S. S., Le Bastard, C., Ihamouten, A., Baltazart, V., Dérobert, X., Fauchard, C., Guilbert, D., & Bosc, F. (2017). Detection of debondings with ground penetrating radar using a machine learning method. In 2017 9th International Workshop on Advanced Ground Penetrating Radar, IWAGPR 2017 - Proceedings (vol. 2017). Institute of Electrical and Electronics Engineers Inc.
48.
Zurück zum Zitat Van Meirvenne, M. (2015). Advanced electric and electromagnetic methods for the characterization of soil. In A. Benedetto & L. Pajewski (Eds.), Civil Engineering Applications of Ground Penetrating Radar (pp. 343–359). Cham: Springer. Van Meirvenne, M. (2015). Advanced electric and electromagnetic methods for the characterization of soil. In A. Benedetto & L. Pajewski (Eds.), Civil Engineering Applications of Ground Penetrating Radar (pp. 343–359). Cham: Springer.
49.
Zurück zum Zitat Vu, T. H., Nguyen, L., Guo, T., & Monga, V. (2018). Deep network for simultaneous decomposition and classification in UWB-SAR imagery. In 2018 IEEE Radar Conference, RadarConf 2018 (pp. 553–558). Piscataway: Institute of Electrical and Electronics Engineers Inc. Vu, T. H., Nguyen, L., Guo, T., & Monga, V. (2018). Deep network for simultaneous decomposition and classification in UWB-SAR imagery. In 2018 IEEE Radar Conference, RadarConf 2018 (pp. 553–558). Piscataway: Institute of Electrical and Electronics Engineers Inc.
50.
Zurück zum Zitat Walker, B., & Ray, L. (2019). Multi-class crevasse detection using ground penetrating radar and feature-based machine learning. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3578–3581) Walker, B., & Ray, L. (2019). Multi-class crevasse detection using ground penetrating radar and feature-based machine learning. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 3578–3581)
51.
Zurück zum Zitat Wang, P., Hu, Z., Zhao, Y., & Li, X. (2016). Experimental study of soil compaction effects on GPR signals. Journal of Applied Geophysics, 126, 128–137.CrossRef Wang, P., Hu, Z., Zhao, Y., & Li, X. (2016). Experimental study of soil compaction effects on GPR signals. Journal of Applied Geophysics, 126, 128–137.CrossRef
52.
Zurück zum Zitat Warren, C., Giannopoulos, A., & Giannakis, I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Computer Physics Communications, 209, 163–170.CrossRef Warren, C., Giannopoulos, A., & Giannakis, I. (2016). gprMax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar. Computer Physics Communications, 209, 163–170.CrossRef
53.
Zurück zum Zitat Williams, R. M., Ray, L. E., & Lever, J. H. (2012). Autonomous robotic ground penetrating radar surveys of ice sheets: Using machine learning to identify hidden crevasses. In 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings (pp. 7–12). Williams, R. M., Ray, L. E., & Lever, J. H. (2012). Autonomous robotic ground penetrating radar surveys of ice sheets: Using machine learning to identify hidden crevasses. In 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings (pp. 7–12).
54.
Zurück zum Zitat Wu, K., Rodriguez, G. A., Zajc, M., Jacquemin, E., Clément, M., De Coster, A., & Lambot, S. (2019). A new drone-borne GPR for soil moisture mapping. Remote Sensing of Environment, 235, 111456.CrossRef Wu, K., Rodriguez, G. A., Zajc, M., Jacquemin, E., Clément, M., De Coster, A., & Lambot, S. (2019). A new drone-borne GPR for soil moisture mapping. Remote Sensing of Environment, 235, 111456.CrossRef
55.
Zurück zum Zitat Yoder, R. E., Freeland, R. S., Ammons, J. T., & Leonard, L. L. (2000). Mapping agricultural fields with GPR and EMI to predict offsite movement of agrochemicals. In Proceedings of SPIE, vol. 4084. Yoder, R. E., Freeland, R. S., Ammons, J. T., & Leonard, L. L. (2000). Mapping agricultural fields with GPR and EMI to predict offsite movement of agrochemicals. In Proceedings of SPIE, vol. 4084.
56.
Zurück zum Zitat Zhang, Y., Xiao, Z., Wu, L., Lu, X., & Wang, Y. (2017). Deep learning for subsurface penetrating super-resolution imaging. In 2017 10th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT) (pp. 1–4). Zhang, Y., Xiao, Z., Wu, L., Lu, X., & Wang, Y. (2017). Deep learning for subsurface penetrating super-resolution imaging. In 2017 10th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT) (pp. 1–4).
57.
Zurück zum Zitat Zheng, J., Teng, X., Liu, J., & Qiao, X. (2019). Convolutional neural networks for water content classification and prediction with ground penetrating radar. IEEE Access, 7, 185385–185392.CrossRef Zheng, J., Teng, X., Liu, J., & Qiao, X. (2019). Convolutional neural networks for water content classification and prediction with ground penetrating radar. IEEE Access, 7, 185385–185392.CrossRef
58.
Zurück zum Zitat Zhou, H., Feng, X., Zhang, Y., Nilot, E., Zhang, M., Dong, Z., & Qi, J. (2018). Combination of support vector machine and H-alpha decomposition for subsurface target classification of GPR. In 2018 17th International Conference on Ground Penetrating Radar, GPR 2018. Piscataway: Institute of Electrical and Electronics Engineers Inc. Zhou, H., Feng, X., Zhang, Y., Nilot, E., Zhang, M., Dong, Z., & Qi, J. (2018). Combination of support vector machine and H-alpha decomposition for subsurface target classification of GPR. In 2018 17th International Conference on Ground Penetrating Radar, GPR 2018. Piscataway: Institute of Electrical and Electronics Engineers Inc.
Metadaten
Titel
Ground-Penetrating Radar-Mounted Drones in Agriculture
verfasst von
Petri Linna
Antti Halla
Nathaniel Narra
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-77860-6_8