Design of a Remote-controlled and GPS-guided Autonomous Robot for Precision Farming

Ünal, İ̇lker; Topakçı, Mehmet

doi:10.5772/62059

Cited by 23 publications

(32 citation statements)

References 17 publications

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“…In order to operate the mobile robot both manually and autonomously, the navigation program was codded in Visual Studio.NET 2015 using Visual Basic.NET language. This program was firstly coded for two-wheel drive robots by the author in 2015 [24], and rearranged to the four-wheel drive robots for this study. However, the navigation algorithm is the same.…”

Section: Autonomous Robot and Steering Algorithmsmentioning

confidence: 99%

“…In quadrant control, the compass dial is used to determine the quadrant in which the current point was located. The calculated azimuth angle helped in determining the quadrant in which the target point was located [24].…”

Section: Autonomous Robot and Steering Algorithmsmentioning

confidence: 99%

“…The differential steering mechanism is used. It can be steered point-to-point both autonomously and manually [24]. 3.…”

mentioning

confidence: 99%

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Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Ünal

Kabaş

Sözer

2020

Sensors

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Soil electrical resistivity (ER) is an important indicator to indirectly determine soil physical and chemical properties such as moisture, salinity, porosity, organic matter level, bulk density, and soil texture. In this study, real-time ER measurement system has been developed with the help of an autonomous robot. The aim of this study is to provide rapid measurement of the ER in large areas using the Wenner four-probe measurement method for precision farming applications. The ER measurement platform consists of the Wenner probes, a y-axis shifter driven by a DC motor through a gear reducer, all installed on a steel-frame that mount to an autonomous robot. An embedded industrial computer and differential global positioning system (DGPS) were used to assist in real-time measuring, recording, mapping, and displaying the ER and the robot position during the field operation. The data acquisition software was codded in Microsoft Visual Basic.NET. Field experiments were carried out in a 1.2 ha farmland soil. ER and DGPS values were stored in Microsoft SQL Server 2005 database, an ordinary Kriging interpolation technique by ArcGIS was used and the average ER values were mapped for the soil depth between 0 and 50 cm. As a result, ER values were observed to be between 30.757 and 70.732 ohm-m. In conclusion, the experimental results showed that the designed system works quite well in the field and the ER measurement platform is a practical tool for providing real-time soil ER measurements.

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Section: Autonomous Robot and Steering Algorithmsmentioning

confidence: 99%

Section: Autonomous Robot and Steering Algorithmsmentioning

confidence: 99%

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Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Ünal

Kabaş

Sözer

2020

Sensors

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“…Prototypes of autonomous vehicles performing farming tasks in the field exist already [ 9 , 10 , 29 , 30 ]. However, putting the “brains” into such agents is still a hard challenge and success is limited to a crop’s specifics and the task at hand.…”

Section: Introductionmentioning

confidence: 99%

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

et al. 2020

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A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.

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“…The position control and trajectory tracking of mobile robots with non-holonomic constraints has been a problem of great interest to many researchers and the literature provides various methods for dealing with it [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Nevertheless, in an agricultural context, some previous approaches have attempted to solve these issues [11][12][15][16], but none have truly succeeded. This is largely due to the unknown and stochastic agricultural environments mobile robots have to work in, requiring them to have high levels of adaptation and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Based on the Selection of Fuzzy Behaviours for following Trajectories in Crops

Urrea

Muñoz

2016

International Journal of Advanced Robotic Systems

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This article addresses the problem of trajectory tracking in crops by a weed sprayer mobile robot (WSMR). This problem arises because to fumigate, the robot must follow a predefined path and avoid any obstacles it may encounter. To achieve both trajectory tracking and obstacle avoidance, a control scheme based on different behaviours is proposed, which consists essentially of an adaptive controller with a reference model for trajectory tracking and a fuzzy reactive for obstacle avoidance. Each of these controllers is executed according to the selection of the fuzzy behaviour controller, which uses information delivered by anticollision sensors located on the robot. As a result of the implementation of this behaviour-based architecture and by means of computer simulations and experimental laboratory tests, the WSMR demonstrates the capability of autonomously following a desired trajectory between the rows of a crop in the presence of obstacles. The results are evaluated by taking into account trajectory tracking curves and the operating requirements of each controller, as well as the application of different errors indices for quantitatively evaluating the proposed control scheme.

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Design of a Remote-controlled and GPS-guided Autonomous Robot for Precision Farming

Cited by 23 publications

References 17 publications

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

Mobile Robot Based on the Selection of Fuzzy Behaviours for following Trajectories in Crops

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