Model of artificial neural network in increasing the efficiency of harvesting potatoes by quality embedding of the planting material

Abstract

The technological process of the harvester operation, regardless of the harvesting technology, is achieved, among other things, by the high-quality preparation of the field for harvesting vegetables and potatoes. It has been established that the exclusion of the content of soil and plant impurities in the harvested products contributes to an increase in its storage, which cannot be ensured if there are free tops and weeds in the heap up to 2 ... 4%. At the same time, in order to exclude the ingress of soil impurities to the cleaning devices of the harvesting machine, it is necessary to ensure the high-quality work of the digging working body of root and tuber crops with underground fruiting. In addition, due to the fact that the performance of the root-extracting and separating devices of the harvesting machine is affected by the work of the furrow-closing working bodies of the planting machine, since the imperfect process of planting tubers in the furrow with soil leads to uneven maturation of tubers, as well as an increased rise of the soil layer during cleaning. The results of studies on the development of a functional diagram of a machine for planting potato tubers in the form of an artificial neural network, dependencies for determining the predictive indicators of the quality of the machine for planting potatoes, optimal technological parameters of a disk incorporating body for determining the amount of soil required for high-quality incorporation of potato tubers are presented.