Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images
The paper focuses on the methodology for studying inhomogeneities in crop development based on the analysis of multispectral images obtained during the Earth remote sensing. The methodology is based on calculation of the Normalized Difference Vegetation Index (NDVI) and its analysis at different sta...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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FRUCT
2019-11-01
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| Series: | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| Subjects: | |
| Online Access: | https://fruct.org/publications/fruct25/files/Sko.pdf
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| _version_ | 1818033601253474304 |
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| author | Petr Skobelev Vladimir Galuzin Vitaly Travin Anastasiya Galitskaya Elena Simonova |
| author_facet | Petr Skobelev Vladimir Galuzin Vitaly Travin Anastasiya Galitskaya Elena Simonova |
| author_sort | Petr Skobelev |
| collection | DOAJ |
| description | The paper focuses on the methodology for studying inhomogeneities in crop development based on the analysis of multispectral images obtained during the Earth remote sensing. The methodology is based on calculation of the Normalized Difference Vegetation Index (NDVI) and its analysis at different stages of agricultural production. The authors propose an original method and new algorithms for detecting inhomogeneities that take into account field zoning at various stages of the plant development cycle: preparing the field for sowing, emergence and development of seedlings, heading. A mathematical description of the agricultural field as an object of study is given, as well as characteristics of inhomogeneities at each stage of agricultural development. The developed algorithms are used in the Smart Farming system, designed to solve the problems of precision farming in the image processing software module. Comparative analysis of results obtained by the module and by similar systems has proven high efficiency of the developed methodology. |
| first_indexed | 2024-12-10T06:25:51Z |
| format | Article |
| id | doaj.art-9cc950eecbcd425b84acfdeac15cc1cf |
| institution | Directory Open Access Journal |
| issn | 2305-7254 2343-0737 |
| language | English |
| last_indexed | 2024-12-10T06:25:51Z |
| publishDate | 2019-11-01 |
| publisher | FRUCT |
| record_format | Article |
| series | Proceedings of the XXth Conference of Open Innovations Association FRUCT |
| spelling | doaj.art-9cc950eecbcd425b84acfdeac15cc1cf2022-12-22T01:59:13ZengFRUCTProceedings of the XXth Conference of Open Innovations Association FRUCT2305-72542343-07372019-11-0162225272278Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing ImagesPetr Skobelev0Vladimir Galuzin1Vitaly Travin2Anastasiya Galitskaya3Elena Simonova4Samara State Technical University, Samara, RussiaSamara State Technical University, Samara, RussiaSEC «Smart Solutions», Ltd Samara, RussiaSEC «Smart Solutions», Ltd Samara, RussiaSamara National Research University, Samara, RussiaThe paper focuses on the methodology for studying inhomogeneities in crop development based on the analysis of multispectral images obtained during the Earth remote sensing. The methodology is based on calculation of the Normalized Difference Vegetation Index (NDVI) and its analysis at different stages of agricultural production. The authors propose an original method and new algorithms for detecting inhomogeneities that take into account field zoning at various stages of the plant development cycle: preparing the field for sowing, emergence and development of seedlings, heading. A mathematical description of the agricultural field as an object of study is given, as well as characteristics of inhomogeneities at each stage of agricultural development. The developed algorithms are used in the Smart Farming system, designed to solve the problems of precision farming in the image processing software module. Comparative analysis of results obtained by the module and by similar systems has proven high efficiency of the developed methodology.https://fruct.org/publications/fruct25/files/Sko.pdf earth remote sensingmultispectral imagesimage processingbinary imageindex imagedetermining inhomogeneity |
| spellingShingle | Petr Skobelev Vladimir Galuzin Vitaly Travin Anastasiya Galitskaya Elena Simonova Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images Proceedings of the XXth Conference of Open Innovations Association FRUCT earth remote sensing multispectral images image processing binary image index image determining inhomogeneity |
| title | Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images |
| title_full | Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images |
| title_fullStr | Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images |
| title_full_unstemmed | Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images |
| title_short | Determining Inhomogeneity Areas in Agricultural Fields Based on the Earth Remote Sensing Images |
| title_sort | determining inhomogeneity areas in agricultural fields based on the earth remote sensing images |
| topic | earth remote sensing multispectral images image processing binary image index image determining inhomogeneity |
| url | https://fruct.org/publications/fruct25/files/Sko.pdf
|
| work_keys_str_mv | AT petrskobelev determininginhomogeneityareasinagriculturalfieldsbasedontheearthremotesensingimages AT vladimirgaluzin determininginhomogeneityareasinagriculturalfieldsbasedontheearthremotesensingimages AT vitalytravin determininginhomogeneityareasinagriculturalfieldsbasedontheearthremotesensingimages AT anastasiyagalitskaya determininginhomogeneityareasinagriculturalfieldsbasedontheearthremotesensingimages AT elenasimonova determininginhomogeneityareasinagriculturalfieldsbasedontheearthremotesensingimages |