This paper discusses the applicability of optical and vibrational spectroscopies for the identification and characterization of the T-2 mycotoxin. Vibrational states and electronic structure of the T-2 toxin molecules are simulated using a density-functional quantum-mechanical approach. A numerical experiment aimed at comparing the predicted structural, vibrational and electronic properties of the T-2 toxin with analogous characteristics of the structurally similar 3-deacetylcalonectrin is performed, and the characteristic spectral features that can be used as fingerprints of the T-2 toxin are determined. It is shown that theoretical studies of the structure and spectroscopic features of trichothecene molecules facilitate the development of methods for the detection and characterization of the metabolites.

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.

Crop monitoring with air monitoring tools on the basis different types of unmanned aerial vehicles with a payload allows to identify problem areas in the field and make timely management decisions. Such complexes suggest a simple and cheap method to method of collecting information about agricultural biological objects. Every season, due to the lack of timely and accurate information about the condition of farmland, agricultural producers lose profits. The use of UAV platforms in agriculture ultimately allows to predict yields and reduce losses. Consequently, the integration of multispectral cameras on UAVs becomes an actual and important task. The main goal of this work is to develop a gimbal for installing the Parrot Sequoia multispectral camera on the DJI Phantom 4 Pro UAV and connecting it to the standard power plant. During the development, a gimbal was made that takes into account all sensors and has dampers for the camera, a method for powering a multispectral camera from the UAV’s onboard power supply, flight tests were conducted that showed stable work of the UAV with additional attachments.

Aim of the state’s programs is development of selection and seed production in Russia. It is required researching related to the creation of new intelligent technical systems which implemented in techniques for selection, in particular in a self-propelled selection seeder. It is proposed to develop an intelligent seed sowing technology and it will be realize on the basis of a self-propelled selection seeder.

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.

Optical sensors have shown good capabilities for detecting and monitoring plant diseases, including fusariosis. The spectral characteristics of the excitation and luminescence of wheat, oat and barley seeds were measured using a diffraction spectrofluorimeter in the range of 180–700 nm. It was found that during infection, the spectral density of the absorption capacity increases and the curve η_e(λ) shifts upwards in the range of 380–450 nm. The shift to the left is also noticeable for the wheat and barley spectra. The photoluminescence flux at λ_e = 232 nm increased by 1.71 times when oat seeds were infected, by 2.63 times when wheat was infected and by 3.14 times when barley was infected. The dependences of the infection degree on the photoluminescence flux are statistically and reliably approximated by linear regression models with determination coefficients R² = 0.83–0.95. The method of determining the degree of infection can include both absolute measurements of photoluminescence flux in the range of 290–380 nm and measurements of the flux ratios when excited by radiation of 232 nm and 424 nm for wheat and 485 nm for barley. An optoelectronic device for remote monitoring can be designed in order to implement the methodology for determining the degree of infection of agricultural plant seeds.