Methods of operational accounting of weed inflammation of fields using digital agricultural technologies in Yakutia

In the permafrost floodplain soils of Yakutia, research was conducted in 2024 to study the distribution of weed vegetation using digital technologies. The purpose of the research was to determine the weediness of fields in Yakutia using unmanned aerial vehicles (UAVs) on the example of the Khangalassky District of the Sakha Republic (Yakutia). The subject of the research was to determine the weediness of fields in Yakutia using UAVs. Field work was conducted using a multi-rotor quadcopter (drone) called DJIMavics 3 during the vegetation period of forage crops. The studies were conducted during the vegetation period of the main fodder crops. Statistical data processing using the Excel software package, and map drafting using the Agisoft Metashape software. According to the research objectives, field and laboratory studies were conducted to determine the level of weed infestation in the Oktemsky village of the Khangalassky District of the Sakha Republic (Yakutia). The operational accounting by the DJIMavik-c drone made it possible to determine the biomass index of vegetation within the range of 0.2…0.8 based on the color tones. It was found that the Normalized difference vegetation index (NDVI) remained at the same level of 0.3 at the end of the vegetation period (on September 5 and 17) in the first area with perennial grasses. The NDVI of the weed vegetation varied from 0.5 (on September 5) to 0.32-0.4 (on September 17), which is typical for the autumn period. In the second plot, the survey conducted on August 21 showed that the NDVI in oat crops reached a high level of 0.7 with traces of weediness, and then decreased to 0.5 on September 15. In the third plot, the survey conducted from August 19 to September 24 revealed that the NDVI increased from 0.4 to 0.7 in the dynamics of oat plant development from the tillering phase to the beginning of earing, while traces of weediness were observed at 13%. To more accurately determine the condition of the crop map and the percentage of weediness in agricultural crops, it is necessary to combine the use of UAVs with traditional methods of crop weediness assessment.