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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 6, pp. 67-79

Specialized dataset of multispectral aerophotos for solving precision farming problems using artificial intelligence methods

O.A. Mitrofanova 1 , E.P. Mitrofanov 1 , I.S. Blekanov 2 , A.E. Molin 2 
1 Agrophysical Research Institute, Saint Petersburg, Россия
2 Saint Petersburg State University, Saint Petersburg, Russia
Accepted: 06.10.2023
DOI: 10.21046/2070-7401-2023-20-6-67-79
Due to fast evolution of information technology, the task of creating large quality remote sensing datasets is becoming increasingly important. At the Agrophysical Research Institute (AFI), artificial intelligence methods and image analysis have been used for about 20 years. During this period, a large amount of information has been collected to solve the problems of precision farming. The object of the presented study is an experimental AFI bio-polygon, located in Leningrad Region, which consists of 29 fields. For collection, an unmanned aerial vehicle developed by the AFI, as well as an unmanned aerial system Geoscan-401, was used. The shooting was carried out in five spectra: red, green, blue, infrared and red edge, the average flight height was 80 meters, the spatial resolution of the images was 1–10 cm/pixel. The paper considers in detail the generated algorithms of data collection and preprocessing. In the study, as a demonstration of the applicability of the created marked-up dataset, an experiment was conducted to analyze the orthophotomap of one of the fields of the biopolygon for the period 2019–2021, compiled from images of the Micasense RedEdge MX multispectral camera. The method of the classical Random Forest machine learning algorithm adapted to the task and the deep learning method based on the U-Net architecture were used as image analysis methods. The results of the experiment demonstrated the advantage of the deep learning method in solving the problem of determining the nitrogen regime of crops for differentiated fertilization.
Keywords: dataset, image analysis, multispectral aerial photography, precision farming, machine learning, deep learning
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