Information system for monitoring number of fruits and their condition using computer vision technology

¹ Omsk State Agrarian University named after P.A. Stolypin, 1, Institutskaya ploschad, Omsk, 644008, Russian Federation
² Omsk State Technical University (OmSTU), Omsk, Russian Federation

^* Corresponding author: av.shimokhin@omgau.org

Abstract

The main objectives of process automation are to improve the quality and productivity of process execution. An automated process has more stable characteristics than a manually executed process. One of the tools of automation is artificial intelligence in general and computer vision in particular. Computer vision (also called machine vision) is a scientific trend in the field of artificial intelligence and related technologies for acquiring images of real-world objects, processing them and using the obtained data to solve various kinds of applied tasks without (full or partial) human participation. From an engineering perspective, computer vision seeks to automate tasks that can be performed by the human visual system. Computer vision tasks include ways of acquiring, processing, analysing, and understanding digital images and extracting data from the real world to produce numerical or symbolic information. In the context of crop farming, automation is primarily capable of increasing yields and improving the quality of the final product, by better controlling environmental conditions and timely detection of any abnormalities in the development and growth of plants and fruits, by increasing productivity, by replacing a variety of more accurate and responsive automated systems (e.g. irrigation or temperature control) with human involvement, and thereby simplifying the role of humans in cultivation, and by improving the quality of the final product. In the paper, the main objective of the designed information system is the recognition in a digital image of plant fruits with subsequent classification based on known colour ranges in advance. In order to achieve this goal, the following tasks should be solved: minimisation of possible negative effects of distortions of the original image; analysing the image to look for tomato fruits on it.