Purpose: The purpose of this paper is to present a novel application for a newly developed Technical Vision System (TVS), which uses a laser scanner and dynamic triangulation, to determine the vitality of agriculture vegetation. This vision system, installed on an unmanned aerial vehicle, shall measure the reflected laser energy and thereby determine the normalized differenced vegetation index. Design/methodology/approach: The newly developed TVS shall be installed on the front part of the unmanned aerial vehicle, to perform line-by-line scan in the vision system field-of-view. The TVS uses high-quality DC motors, instead of previously researched low-quality DC motors, to eliminate the existence of two mutually exclusive conditions, for exact positioning of a DC motor shaft. The use of high-quality DC motors reduces the positioning error after control. Findings: Present paper emphasizes the exact laser beam positioning in the field-of-view of a TVS. By use of high-quality instead of low-quality DC motors, a significant reduced positioning time was achieved, maintaining the relative angular position error less than 1 per cent. Best results were achieved, by realizing a quasi-continuous control, using a high pulse-width modulated duty cycle resolution and a high execution frequency of the positioning algorithm. Originality/value: The originality of present paper is represented by the novel application of the newly developed TVS in the field of agriculture. The vitality of vegetation shall be determined by measuring the reflected laser energy of a scanned agriculture zone. The paper’s main focus is on the exact laser beam positioning within the TVS field-of-view, using high-quality DC motors in closed-loop position control configuration.

Many laser scanners depend on their mechanical construction to guarantee their measurements accuracy, however, the current computational technologies allow us to improve these measurements by mathematical methods implemented in neural networks. In this article we are going to introduce the current laser scanner technologies, give a description of our 3D laser scanner and adjust their measurement error by a previously trained feed forward back propagation (FFBP) neural network with a Widrow-Hoff weight/bias learning function. A comparative analysis with other learning functions such as the Kohonen algorithm and gradient descendent with momentum algorithm is presented. Finally, computational simulations are conducted to verify the performance and method uncertainty in the proposed system.

This paper introduces a novel electronic circuit that has to be embedded in a photodiode sensor as an integrated circuit board for electronic signal processing that detects the energy center of an optical signal, which represents the most accurate position measurement from a light emitter source mounted on a structure (such as a building, a bridge, or a mine). The optical scanning sensor for structural health monitoring proposed is a flexible system that can operate with a coherent or incoherent light emitter source. It is conformable to any kind of structure surfaces and data storage budget thanks to the signal processing stage being embedded into the sensor and does not require additional software processing, which reduces the time and memory spacing requirements for information recording. The theoretical principle of operation, as well as the technological and experimental aspects of design, development, and validation is presented.