The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

Технология внесения пестицидов и удобрений беспилотными летательными аппаратами в цифровом сельском хозяйстве

A compact and low-weight LIDAR instrument has been developed for laser-induced fluorescence spectroscopy sensing of maize fields. Fluorescence LIDAR had to be installed on a small industrial drone so that its mass was <2 kg and power consumption was <5 W. The LIDAR instrument utilized a continuous wave diode laser (405 nm, 150 mW) for inducing fluorescence and a small spectrometer for backscattered photons acquisition. For field testing, the LIDAR instrument was installed on a quadcopter for remote sensing of plants in a maize field in three periods of the plant’s life. The obtained fluorescence signal maps have demonstrated that the average chlorophyll content is rather non-uniform over the field and tends to increase through the plant vegetation cycle. Field tests proved the feasibility and perspectives of autonomous LIDAR sensing of agricultural fields from drones for the detection and location of plants under stress.

The paper demonstrates that positioning an unmanned aerial vehicle (UAV) in controlled agricultural environments is possible without reliance on satellite navigation. Modifications to the standard flight controller software, specifically, the integration of modules for processing and decoding data from a video stream sensor and a laser rangefinder, ensures high accuracy in determining coordinates both in height and in plan. (Research purpose) The study aims to determine the positioning accuracy parameters of a UAV when using a video stream sensor and a laser rangefinder as primary instruments for coordinate calculation. (Materials and methods) Data from the Optical Flow & LIDAR Sensor 3901-L0X, transmitted to the UAV flight controller via the debugger port, were analyzed. The Canny detector and Gaussian filter were applied to extract precise contours of high-contrast objects on a horizontal plane and to compute the coordinates of multiple points in the processed video stream. The scaling factor of these coordinates was determined based on laser rangefinder measurements. Methods of mathematical statistics were used to process the research data and calculate errors in determining positioning coordinates. (Results and discussion) The findings indicate that combining data from the video stream sensor with height measurements from the laser rangefinder yields high accuracy and enables aerial imaging of agricultural biological objects in greenhouse environments. (Conclusions) The study determined that the software for processing video stream and laser rangefinder data enables aerial imaging in greenhouse environments, achieving UAV spatial coordinate calculation accuracy exceeding 95 percent.

Detection and mapping of Sosnowsky’s hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student’s t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication.