Using Neural Networks to Identify Technological Stress Using the Example of Crop Compaction

The article is devoted to the study of the use of neural networks to identify the technological stress of plantations in the technologies of precision agriculture. The study takes into account such complex aspects of sample selection as the speed of image acquisition, the effectiveness of assessing the state of crop compaction, etc. The use of neural networks makes it possible to automate and increase the accuracy of selection, to improve the quality of the analysis of plant stands, provided that the technology of evaluating soil samples is followed. The obtained results indicate the prospects of implementing this approach in modern agriculture.