Quality Metrics of Automated Machinery in Potato Plant Cultivation for Breeding and Seed Production

Abstract

The paper notes the significance of promptly identifying infected plants when cultivating potatoes for breeding and seed production. Consequently, there is a need to undertake a series of initiatives aimed at developing a digital system for automated detection and recognition of both healthy and infected plants. (Research purpose) The research aims to determine the patterns of changes in the quality indicators of the machinery employed in cultivating potato plants. (Materials and methods) The research was carried out on the area of the selection-experimental plot. A system of criteria was developed to evaluate the identification of infected plants. (Results and discussion) The research assisted in identifying the required reliability of the measuring operation for the machine vision system and aided in predicting its current state for identifying infected plants. This was achieved by analyzing statistical data on the distribution of the indirect parameter (indications of infection on the inside of the plant leaf) and considering the margin of error in its measurements. The reliability of the system for identifying infected plants depends on the precision of technical instruments used to gauge the plant’s condition, the methodologies employed in measurement, the software utilized for processing the obtained data, and other parameters. (Conclusions) Measurement information management involves making a judicious selection of an indirect parameter that guarantees the precision of identifying infected plants with a confidence interval of 0.95. It is revealed that in the initial training epoch of the infected plant identification system, the accuracy of plant classification stood at 0.797, equivalent to 79.7 percent for all plants. The correctness of infected plant recognition was 0.607 or 60.7 percent. Moreover, the accuracy of correctly identifying infected plants was determined to be 0.607, or 60.7 percent. Notably, by this epoch, the accuracy of recognizing healthy plants had already reached 99.9 percent.