The conducted analysis of the technical provision of machine-tractor fleet of Russian agricultural enterprises for the implementation of crop production technologies revealed a lack of technical support of agricultural production, which leads to a reduction in acreage or stretching of agricultural terms of technological operations. All this leads to a significant loss of agricultural products. The qualitative conditions of enterprises are described by four stable zones, which are directly dependent on the level of technical equipment of production: a zone of minimizing the scale of production, and in many cases the termination of the enterprise’s operation; zone of extended agrotechnical terms of technological operations; zone of optimal agrotechnical terms established by the requirements of technological maps; zone of strengthening the protective properties of the enterprise and increasing the reliability of its operation. The method of determining the need for agricultural machinery was developed, which showed that the best level of technical provision is a level that ensures reliable performance of all technological operations in the optimal agrotechnical terms, on the basis of which the boundary conditions of technical security are developed.

In this paper, we propose an approach to the classification of high-resolution hyperspectral images in the applied problem of identification of vegetation types. A modified spectral-spatial convolutional neural network with compensation for illumination variations is used as a classifier. For generating a training dataset, an algorithm based on an adaptive vegetation index is proposed. The effectiveness of the proposed approach is shown on the basis of survey data of agricultural lands obtained from a compact hyperspectral camera developed in-house.

Use of a variety of electrotechnics is a technologically-efficient and environmentally-friendly technique that increases the productivity of cultivated plants. Stimulation of green plants and vegetable crops in electric field with the intensity of 5-50 kV/m made it clear that the maximum efficiency is observed in the growth period – an increase of up to 30%, compared to the control. Plants have been subjected to stimulation for 3 hours twice a day (in the morning and in the evening). Analysis of studies on the pre-seeding seed stimulation showed that the exposure to pulsed magnetic field improves the dynamics of germination and plant growth at the early stages of development by an average of 10-20%, and more uniform germination helps to ensure high yields.