Development of mounted system for controlling row crop cultivator in automatic mode

Abstract

Weed control automation appears to be a very promising technology based on the tremendous advances in computer processing, machine vision and robotics. Machine vision systems are based on size differences between crops and weeds and or on the regular structure of crop rows, allowing the system to recognize crop plants and control surrounding weeds. The paper provides description of the mounted system for orienting the row cultivator in rows and its displacement relative to the tractor using vision systems and automatic control. The developed technical vision system is capable of clearly identifying sugar beet leaves based on the deep learning artificial neural networks technology, and a specially developed algorithm for identifying the center of the row spacing to control the moving part of the cultivator in the required direction to level the inaccuracy of the tractor. The use of controlled mounted device with a tractor and an implement based on vision systems, the accuracy of technological operations for the row crops care, will increase the technical level of the machine and tractor unit, and reduce labor costs. Technical vision systems and automatic control of the cultivator will improve the quality of inter-row cultivation of row crops and reduce the pesticide load on the environment. The results of these studies can be used to create machines for cultivation of agricultural crops with an automated control system.