• Vitalii Lysenko
  • Oleksiy Opryshko
  • Dmytro Komarchuk
  • Natalia Pasichnyk
  • Natalya Zaets
  • Alla Dudnyk



UAV, monitoring, nutrition, fertilizers, drones.


The 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.


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How to Cite

Lysenko, V., Opryshko, O., Komarchuk, D., Pasichnyk, N., Zaets, N., & Dudnyk, A. (2018). INFORMATION SUPPORT OF THE REMOTE NITROGEN MONITORING SYSTEM IN AGRICULTURAL CROPS. International Journal of Computing, 17(1), 47-54.