scholarly journals Agricultural Vehicle Robot

2018 ◽  
Vol 30 (2) ◽  
pp. 165-172 ◽  
Author(s):  
Noboru Noguchi ◽  

With the intensive application of techniques in global positioning, machine vision, image processing, sensor integration, and computing-based algorithms, vehicle automation is one of the most pragmatic branches of precision agriculture, and has evolved from a concept to be in existence worldwide. This paper addresses the application of robot vehicles in agriculture using new technologies.

2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.


2014 ◽  
Vol 711 ◽  
pp. 333-337 ◽  
Author(s):  
Fang Wang ◽  
Chao Kun Ma ◽  
Shan Qiang Dai ◽  
Chang Chun Li

The author has designed a visual workbench to realize that the rim can rotate with the workbench. Meanwhile, the linear CCD camera records the rim’s circumference. The paper has explored the measurement method of getting the rim valve hole position using machine vision. The system can calculate the position of rim-hole and control the servo motor by image processing, characteristic recognizing and measuring to locate the position of wheel-hole automatically. And the paper has verified the accuracy of the method by experiments.


2000 ◽  
Vol 80 (3) ◽  
pp. 405-413 ◽  
Author(s):  
L.W. Turner ◽  
M.C. Udal ◽  
B. T. Larson ◽  
S.A. Shearer

Precision agriculture is already being used commercially to improve variability management in row crop agriculture. In the same way, understanding how spatial and temporal variability of animal, forage, soil and landscape features affect grazing behavior and forage utilization provides potential to modify pasture management, improve efficiency of utilization, and maximize profits. Recent advances in global positioning system (GPS) technology have allowed the development of lightweight GPS collar receivers suitable for monitoring animal position at 5-min intervals. The GPS data can be imported into a geographic information system (GIS) to assess animal behavior characteristics and pasture utilization. This paper describes application and use of GPS technology on intensively managed beef cattle, and implications for livestock behavior and management research on pasture. Key words: Livestock behavior, electronics, grazing, forage, global positioning system, geographic information system


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Majid Amirfakhrian ◽  
Mahboub Parhizkar

AbstractIn the next decade, machine vision technology will have an enormous impact on industrial works because of the latest technological advances in this field. These advances are so significant that the use of this technology is now essential. Machine vision is the process of using a wide range of technologies and methods in providing automated inspections in an industrial setting based on imaging, process control, and robot guidance. One of the applications of machine vision is to diagnose traffic accidents. Moreover, car vision is utilized for detecting the amount of damage to vehicles during traffic accidents. In this article, using image processing and machine learning techniques, a new method is presented to improve the accuracy of detecting damaged areas in traffic accidents. Evaluating the proposed method and comparing it with previous works showed that the proposed method is more accurate in identifying damaged areas and it has a shorter execution time.


2017 ◽  
Vol 5 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Dimitris Kaimaris ◽  
Petros Patias ◽  
Maria Sifnaiou

Purpose The purpose of this paper is to discuss unmanned aerial vehicle (UAV) and the comparison of image processing software. Design/methodology/approach Images from a drone are used and processed with new digital image processing software, Imagine UAV® of Erdas imagine 2015®. The products (Digital Surface Model and ortho images) are validated with check points (CPs) measured in the field with Global Positioning System. Moreover, similar products are produced by Agisoft PhotoScan® software and are compared with both the products of Imagine UAV and the CPs. Findings The results reveal that the two software tools are almost equivalent, while the accuracies of their products are similar to the accuracies of the external orientations of drone images. Originality/value Comparison of image processing software.


Author(s):  
Milan Sonka ◽  
Vaclav Hlavac ◽  
Roger Boyle

Author(s):  
Akalpita Tendulkar

The global population is increasing at a tremendous speed; thus, the demand for safe and secure food to meet this population is in demand. Therefore, traditional farming methods are insufficient to meet this demand; thus, the next revolution in agriculture is required, which is Precision Agriculture (PA), the Fourth Agriculture Revolution. PA is a technology where the concept of farm management is based on observation, measuring, and responding to inter- and intra-field variability in crops. The technologies used for performing precision agriculture are mapping, global positioning system (GPS), yield monitoring and mapping, grid soil sampling application, variable-rate fertilizer application, remote sensing, geographic information systems (GIS), quantifying on farm variability, soil variation, variability of soil water content, time and space scales, robots, drones, satellite imagery, the internet of things, smartphone, and machine learning. Hence, the current chapter will be emphasizing the overview, concepts, history, world interest, benefits, disadvantages, and precision farming needs.


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