Izvorni znanstveni članak

https://doi.org/10.18047/poljo.28.2.10

THE EVALUATION OF THE RGB AND MULTISPECTRAL CAMERA ON THE UNMANNED AERIAL VEHICLE (UAV) FOR THE MACHINE LEARNING CLASSIFICATION OF MAIZE

Mladen Jurišić ; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31000 Osijek, Hrvatska
Dorijan Radočaj ; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31000 Osijek, Hrvatska
Ivan Plaščak ; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31000 Osijek, Hrvatska
Daria Galić Subašić ; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31000 Osijek, Hrvatska
Davor Petrović ; Sveučilište Josipa Jurja Strossmayera u Osijeku, Fakultet agrobiotehničkih znanosti Osijek, Vladimira Preloga 1, 31000 Osijek, Hrvatska

Sažetak

This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural parcel near Koška. The highest overall accuracy was obtained in the combination of the red edge (RE), near-infrared (NIR), and normalized difference vegetation index (NDVI) in both subsets, with a 99.8% and 91.8% overall accuracy, respectively. The conducted analysis proved that the RGB camera obtained sufficient accuracy and was an acceptable solution to the soil and vegetation classification. Additionally, a multispectral camera and spectral analysis allowed for a more detailed analysis, primarily of the spectrally similar areas. Thus, this procedure represents a basis for both the crop density calculation and weed detection while deploying an unmanned aerial vehicle. To ensure crop classification effectiveness in practical application, it is necessary to further integrate the weed classes in the current vegetation class and separate them into crop and weed classes.

Ključne riječi

crop density; Random Forest; supervised classification; spectral analysis; normalized difference vegetation index (NDVI)

Hrčak ID:

289119

URI

https://hrcak.srce.hr/289119

Datum izdavanja:

20.12.2022.

Podaci na drugim jezicima: hrvatski

Posjeta: 698 *