Chapters authored
Financial Feasibility Analysis of Natura Rab Business: Case Study
In 2015 Natura Rab decided to provide three very important investments that will greatly change and facilitate its future business activities, especially the first project. The first and largest financial investment is the construction of the new organic shop with products at the central farm called Natura Rab. The second investment project is the new 2500 m2 olive plantation. The third investment in the analyzed family company is related to the beekeeping sector, and it involves several activities like buying new beekeeping equipment and new work vehicle. Before implementing the three investment projects, some financial parameters for the further assessment of investments were used, such as the net present value (NPV) and the internal rate of return (IRR). The investment value of the new shop is 38315.88 €, and the annual cash flow is 13,288 €. The net present value at the discount rate of 5.5% in the fourth year is 8260.55 €. The internal rate of return is 14.51%. The investment value for the second project, the new olive plantation, is 6620 €, and the annual cash flow is 2664.02 €. The net present value at the discount rate of 5.5% in the third year is 567.35 €. The internal rate of return is 10.04%. The investment value of the beekeeping sector for this year is 18428.50 €, and the annual cash flow is 41537.20 €. The net present value at the discount rate of 5.5% after the first year is 20943.25 €.
Part of the book: Operations Research
An Evaluation of Three Different Infield Navigation Algorithms
In this chapter, we present and evaluate three different infield navigation algorithms, based on the readings from a LIDAR sensor. All three algorithms are tested on a small field robot and used to autonomously drive the robot between the two adjacent rows of maze plants. The first algorithm is the simplest one and just takes distance readings from the left and right side. If robot is not in the center of the mid-row space, it adjusts its course by turning the robot in the right direction accordingly. The second approach groups the left and right readings into two vertical lines by using least-square fit approach. According to the calculated distance and orientation to both lines, it adjusts the course of the robot. The third approach tries to fit an optimal triangle between the robot and the plants, revealing the most optimal one. Based on its shape, the course of the robot is adjusted. All three algorithms are tested in a simulated (ROS stage) and then in an outdoor (maze test field) environment comparing the optimal line with the actual calculated position of the robot. The tests prove that all three approaches work with an error of 0.041 ± 0.034 m for the first algorithm, 0.07 ± 0.059 m for the second, and 0.078 ± 0.055 m error for the third.
Part of the book: Agricultural Robots