INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS
Keywords:UAV, monitoring, nutrition, fertilizers, drones.
AbstractThe article addresses issues on application of unmanned aerial vehicles (UAV) to monitor nitrogen nutrition through the example of wheat plants. The optical spectral range can be used to monitor exploitation of the UAV. It is recommended to develop specialized spectral indices for such equipment. The article provides calibration curves for nitrogen nutrition monitoring. In the created neural networks, the linear model is represented as a network without intermediate layers, which in the output layer contains only linear elements, the weight corresponds to the elements of the matrix, and the thresholds are the components of the shear vector. During the operation, the neural network actually multiplies the vector of inputs into the matrix of scales, and then adds a vector of displacement to the resulting vector. Results of the research show how to create the specialized RPVI adapted to technological capabilities of UAVs. It has been experimentally proved that input parameters that describe the state of agricultural plantations are regularly distributed. The average statistical characteristics for additive color RGB model is advisable to be the neural network input instead of large sample data volume.
T. Ahamed, L. Tian, Y. Zhang, K. Ting, “A review of remote sensing methods for biomass feedstock production,” Biomass & Bioenergy, vol. 35, no. 7, pp. 2455–2469, July 2011.
I. Herrmann, A. Karnieli, D. Bonfil, Y. Cohen, V. Alchanatis, “SWIR-based spectral indices for assessing nitrogen content in potato fields,” International Journal of Remote Sensing, vol. 31, no. 19, pp. 5127-5143, January 2010.
T. M. Shadchina, “Elaboration of theoretical bases and methods of the remote sensing of winter wheat crops using the high resolution spectrometry,” Manuscript, Thesis for Dr. Sci (Biol.) by speciality 03.00.12 – Plant Physiology, Institute of Plant Physiology and Genetics, National Academy of Sciences of Ukraine, Kyiv, 1999.
L. S. Bernstein, S. M. Adler-Golden, R. L. Sundberg, et al., “Validation of the QUick Atmospheric Correction (QUAC) algorithm for VNIR-SWIR multi- and hyperspectral imagery,” Proceedings of SPIE, vol. 5806, pp. 668-678, 2005.
K. Soudani, G. Hmimina, N. Delpierre, J.-Y. Pontailler, M. Aubinet, D. Bonal, B. Caquet, A. de Grandcourt, B. Burban, C. Flechard, D. Guyon, A. Granier, P. Gross, B. Heinesh, B. Longdoz, D. Loustau, C. Moureaux, J.-M. Ourcival, S. Rambal, L. Saint André, E. Dufrêne, “Ground-based network of NDVI measurements for tracking temporal dynamics of canopy structure and vegetation phenology in different biomes,” Remote Sensing of Environment, vol. 123, pp. 234-245, 2012.
D.W. Lamb, D.A. Schneider, M.G. Trotter a, M.T. Schaefer, I.J. Yule, “Extended-altitude, aerial mapping of crop NDVI using an active optical sensor: A case study using a Raptor™ sensor over wheat,” Computers and Electronics in Agriculture, vol. 77, pp. 69-73, 2011.
J. Zhou, L.R. Khot, H.Y. Bahlol, R. Boydston, P. N. Miklas, “Evaluation of ground, proximal and aerial remote sensing technologies for crop stress monitoring,” IFAC-PapersOnLine, vol. 49, no. 16, pp. 22–26, 2016.
Y. Kondratenko, O. Gerasin, & A. Topalov, “A simulation model for robots slip displacement sensors,” International Journal of Computing, vol. 15, issue 4, pp. 224-236, 2016.
M. Patil, T. Abukhalil, S. Patel, & T. Sobh, “UB swarm: hardware implementation of heterogeneous swarm robot with fault detection and power management,” International Journal of Computing, vol. 15, issue 3, pp. 162-176, 2016.
M. Herrero-Huerta, D. Hernández-López, P. Rodriguez-Gonzalvez, D. González-Aguilera, J. González-Piqueras, “Vicarious radiometric calibration of a multispectral sensor from an aerial trike applied to precision agriculture,” Computers and Electronics in Agriculture, vol. 108, pp. 28–38, 2014.
H. Xiang, L. Tian, “An automated stand-alone in-field remote sensing system (SIRSS) for in-season crop monitoring,” Computers and Electronics in Agriculture, vol. 78, no. 1, pp.1-8, 2011.
M. M. Saberioona, M. S. M. Amina, A. R. Anuarb, A. Gholizadehc, A. Wayayokd, S. Khairunniza-Bejoda, “Assessment of rice leaf chlorophyll content using visible bands atdifferent growth stages at both the leaf and canopy scale,” International Journal of Applied Earth Observation and Geoinformation, vol. 32, pp. 35–45, 2014.
V. Lysenko, O. Opryshko, D. Komarchyk, N. Pasichnyk, “Drones camera calibration for the leaf research,” Scientific Journal NUBiP, no. 252, pp. 61–65, 2016.
V. Lysenko, D. Komarchuk, O. Opryshko, N. Pasichnyk, N. Zaets, “Determination of the not uniformity of illumination in process monitoring of wheat crops by UAVs,” in Proceedings of the 4th International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), pp. 265-267, 2017. [Online]. Available: http://ieeexplore.ieee.org/document/8246394/
V. Lysenko, D. Komarchuk, O. Opryshko, N. Pasichnyk, N. Zaets, A. Dudnyk, “Usage of flying robots for monitoring nitrogen in wheat crops,” in Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2017), 2017, vol. 1, pp. 30–34. [Online]. Available: http://ieeexplore.ieee.org/document/8095044/
J. Enciso, M. Maeda, J. Landivar, J. Jung, A. Chang, “A ground based platform for high throughput phenotyping,” vol. 141, pp. 286-291, 2017.
V. Borovikov, Statistica. Data analysis on computer, 2-nd edit, St. Petersburg: Piter, 2003, 688 p.
V. P. Lysenko, V. M. Reshetyuk, V. M. Shtepa, N. A. Zaets, Artificial Intelligence: Fuzzy Logic, Neural Networks, Fuzzy Neural Networks, Genetic Algorithm, Kyyiv: NUBiP Ukraine, 2014, 341 p.
M. Arbib, The Handbook of Brain Theory and Neural Networks, London: MIT Press, 2003, 1309 p.
J. Hertz, A. Krogh, R. Palmer, Wstep do Teorii Obliczen Neuronowych, Wyd. II, Warszawa: WNT, 1995.
A.R. Barren, “Approximation and estimation bounds for artificial neural networks,” Machine Learning, vol. 14, pp. 115-133, 1994.
S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: from Fundamentals to Complex Pattern Recognition, CRC Press, 2006.
How to Cite
LicenseInternational Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:
• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.