ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 3, pp. 110-124

Rice crops remote monitoring and heterogeneities detection algorithm

E.V. Truflyak 1 , S.I. Skubiev 2 , V.V. Tsybulevsky 1 , N.V. Malashikhin 1 
1 Kuban State Agrarian University, Krasnodar, Russia
2 State University of Land Management, Moscow, Russia
Accepted: 21.05.2019
DOI: 10.21046/2070-7401-2019-16-3-110-124
In 2018, the work related to the use of rice seed quality indicators remote research was carried out in the fields of All-Russian Research Institute of Rice (ARRIR). For these purposes some varieties of rice were sown on two specially allocated equivalent plots with different models of seeders: CH-16 (factory version, which was on the farm) and KLEN-1.5P (assembled at Ltd SIE KLEN-AGRO KubSAU). For rice growing season remote monitoring, a DJI Phantom 4 PRO unmanned aerial vehicle was used (the survey was carried out both in the visible spectrum and in the near-IR zone). Ground-based methods of control measurements of the height and number of plants were also used for further comparative analysis of remote monitoring results. Digital terrain model based on aerial photography was used to determine the vegetation cover height by remote methods. Longitudinal and transverse profiles of seedlings on the test areas of specified directions were obtained in the Global Mapper 19.1 software environment. The results of filming in KOMPAS-3D program material processing were also used. When processing Plant Health images of two plots (CH-16 and KLEN-1.5P seeders) obtained by Drone Deploy program, an algorithm, performed in Mathcad 15 program, was used, with the help of which the average value of the image tone density code in the array and the number of pixels in the selected range were determined. As a result of conducted experiments, it was determined that after sowing with KLEN-1.5P seeder comparing to CH-16 seeder based on ground and remote studies, the number of shoots is 53 % more with the same seeding rate, plant height is 17 % more (before harvesting 2 %); panicle length before harvesting is 6 % more; yield is more by 12 centners per hectare.
Keywords: remote sensing, unmanned aerial vehicle (UAV), seeder, rice, tests, quality sowing indicators, algorithm, statistical indicators
Full text


  1. D’yakonov V., Mathcad 2000: uchebnyi kurs (Mathcad 2000: training course), Saint Petersburg: Piter, 2000, 592 p.
  2. Bastiaansen W. G. M., Ali S., A New Crop Yield Forecasting Model Based on Satellite Measurements Applied Across the Indus Basin, Pakistan, Agriculture, Ecosystems and Environment, 2003, Vol. 94, No. 3, pp. 321–340.
  3. Chang K. W., Shen Y., Lo J. C., Predicting Rice Yield Using Canopy Reflectance Measured at Booting Stage, Agronomy J., 2005, Vol. 97, No. 3, pp. 872–878, DOI: 10.2134/agronj2004.0162.
  4. Doraiswamy P. C., Hatfield J. L., Jackson T. J., Akhmedov B., Prueger J., Stern A., Crop Condition and Yield Simulations Using Landsat and MODIS, Remote Sensing of Environment, 2004, Vol. 92, No. 4, pp. 548–559.
  5. Inoue Y., Moran M. S., Horie T., Analysis of Spectral Measurements in Paddy Field for Predicting Rice Growth and Yield Based on a Simple Crop Simulation Model, Plant Production Science, 1998, Vol. 1, No. 4, pp. 269–279.
  6. Inoue Y., Penuelas J., Miyata A., Mano M., Normalized Difference Spectral Indices for Estimating Photosynthetic Efficiency and Capacity at a Canopy Scale Derived from Hyperspectral and CO2 Flux Measurements in Rice, Remote Sensing of Environment, 2008, Vol. 112, No. 1, pp. 156–172.
  7. Kuenzer C., Knauer K., Remote sensing of rice crop areas, Intern. J. Remote Sensing, 2013, Vol. 34, No. 6, pp. 2101–2139.
  8. Shibayama M., Akiyama T., Estimating Grain Yield of Maturing Rice Canopies Using High Spectral Resolution Reflectance Measurements, Remote Sensing of Environment, 1991, Vol. 36, No. 1, pp. 45–53.
  9. Tennakoon S. B., Murty V. V. N., Eiumnoh A., Estimation of Cropped Area and Grain Yield of Rice Using Remote Sensing, Intern. J. Remote Sensing, 1992, Vol. 13, No. 2, pp. 427–439.
  10. Wiegand C., Shibayama M., Yamagata Y., Akiyama T., Spectral Observations for Estimating the Growth and Yield of Rice, Japanese J. Crop Science, 1989, Vol. 58, No. 4, pp. 673–683.
  11. Yang C. M., Su M. R., Correlation of Spectral Reflectance to Growth in Rice Vegetation, 19th Asian Conf. Remote Sensing, Manila, Philippines, Nov. 16–20 1998, 1998, 6 p.
  12. Yang C. M., Chen R. K., Modeling Rice Growth with Hyperspectral Reflectance Data, Crop Science Society of America, 2004, Vol. 44, No. 4, pp. 1283–1290, DOI: 10.2135/cropsci2004.1283.