The Use of Machine Vision in the Diagnosis of Ripening Strawberries

Abstract

The problem of image recognition using methods based on statistical solutions can be divided into two classes: parametric and nonparametric. In the first case there is a set of data on the selection of image criteria, in the second such criteria must be found. Taking into account, the possibilities of obtaining initial data for the identification of agricultural objects, the first option is considered in the article. Possibilities of diagnostics of strawberry growth at a stage of its cultivation in hothouse conditions are presented. Several image classifiers that can be used to solve such problems are investigated. An experiment was performed, which has shown the accuracy of the applied algorithms for diagnosis and recognition of berries, which allowed to set thresholds for the detection of ripe berries. A number of berry recognition methods were compared in order to study the accuracy of berry recognition and response to berry growth.

Devising a technique for measuring torque of electric motors using machine vision

“…In safer areas of application, machine vision is quite actively used as the main informative parameter. For example, in [19], an automatic control system is proposed, which uses algorithms to detect plant growth based on data on changes in the shape and color of the object. Such a system determines the change in the number of pixels of a certain color, which allows simple calculations.…”

“…The above procedures considered in studies [18,19] do not always meet the needs of modern measurement systems due to limitations in image processing speed and the influence of certain destabilizing factors. This requires additional integration processes aimed at using machine vision together with modern methods of measuring rotational parameters.…”

“…where V(s, a) is the signal transformation function at the input of the measuring device with variable gain; F ij are transfer functions connecting various points of the system with the rated values of the parameters. The following expression is derived from equations (19):…”