Effectiveness and profitability of automation technologies in greenhouse productivity and food security

¹ Kadyrov Chechen State University, Grozny, Russia
² Novosibirsk State Agrarian University, Novosibirsk, Russia
³ Kazan State Power Engineering University, Kazan, Russia

^* Corresponding author: gazievalaila@outlook.com

Abstract

This paper examines the innovative impact of an automated system developed by the research and production company Gardens of Chechnya, which combines computer vision technologies and image data analysis methods to effectively assess plant health at the embryonic stage. Traditional visual data analysis methods have been labour-intensive and time-consuming, creating barriers to crop production and quality. The automated system developed for the company's scientific needs, based on computer vision, has excellent accuracy, allowing it to examine plants at a new level and detect even the slightest signs of disease and infection. This innovation speeds up the assessment process, reducing it from days to hours. The mobility of the system allows it to be used in various agricultural conditions, which simplifies the assessment of plant health. By making it easier to assess plant health, this innovation promises increased yields, reduced disease spread and faster results, meeting global goals for food security and sustainable agriculture.