• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.810-819
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• 1993

An autonomous lan vehicle for agriculture(ALVA-II) was developed. A prototype vehicle was made by modifying a commercial tractor. A Navigation sensor system with a geo-magnetic sensor performed the autonomous operations of ALVA-II, such as rotary tilling with headland turnings. A navigation sensor system with a machine vision system was also investigated to control ALVA-II following a work boudnary.

• Journal of Biosystems Engineering
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• v.33 no.1
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• pp.58-62
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• 2008

The need for position information in agriculture is gradually increasing for precise control farm vehicle and effective manage farm land. Though geomagnetic sensor has a lot of merits in estimating heading angle of vehicle because of low costs and sensing ability of magnetic north, it is easy that sensor outputs are distorted in electro magnetic field environment. This study was conducted to develop geomagnetic compass which could be available in measuring relative position from reference point correcting output distorted by external electro magnetic field in a small scale field. Magnetic inducing sensor (PNI's Vector2X) which wound enamel coated copper coil on ferrite core in order to measure and correct earth magnetic field. Magnetic azimuth was corrected using the algorithm which estimated amount of magnetic distortion from the difference between each outputs of magnetic sensors that located on the cross shaped base. Developed auto-tuning magnetic sensor was showed less then 5% as bearing accuracy in the strong magnetic field.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.889-894
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• 2017

The purpose of this paper is to implement smart farm using automatic navigation, short - range wireless communication network technology, and automatic take - off and landing system using unmanned aerial vehicle to maximize the efficiency of grazing farm management. The grazing pasture management system that integrates ICT fusion technology for the activation of the mountain ecological livestock production is expected to contribute to the improvement of the productivity of the grazing livestock, the infrastructure to produce the excellent quality, and the competitiveness of the livestock industry in response to the FTA. And it will contribute to the improvement of career force through the supply to the farmhouse.

• Journal of Biosystems Engineering
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• v.36 no.2
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• pp.116-121
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• 2011

This study was conducted to develop a robust navigator which could be in positioning for precision farming through developing a plural GPS receiver with 4 sets of GPS antenna. In order to improve positioning accuracy by integrating GPS signals received simultaneously, the algorithm for processing plural GPS signal effectively was designed. Performance of the algorithm was tested using a simulation program and a fixed point on WGS 84 coordinates. Results of this study are aummarized as followings. 1. 4 sets of lower grade GPS receiver and signals were integrated by kalman filter algorithm and geometric algorithm to increase positioning accuracy of the data. 2. Prototype was composed of 4 sets of GPS receiver and INS components. All Star which manufactured by CMC, gyro compass made by KVH, ground speed sensor and integration S/W based on RTOS(Real Time Operating System)were used. 3. Integration algorithm was simulated by developed program which could generate random position error less then 10 m and tested with the prototype at a fixed position. 4. When navigation data was integrated by geometrical correction and kalman filter algorithm, estimated positioning erros were less then 0.6 m and 1.0 m respectively in simulation and fixed position tests.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.238-245
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• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.