• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.619-627
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• 2022

In Korea, apples and pears are among the most important agricultural products to farmers who seek to earn money as income. Generally, farmers make decisions at various stages to maximize their income but they do not always know exactly which option will be the best one. Many previous studies were conducted to solve this problem by predicting farmers' income structure, but researchers are still exploring better approaches. Currently, machine learning technology is gaining attention as one of the new approaches for farmers' income prediction. The machine learning technique is a methodology using an algorithm that can learn independently through data. As the level of computer science develops, the performance of machine learning techniques is also improving. The purpose of this study is to predict the income structure of apples and pears using the automatic machine learning solution H2O.AI and to present some implications for apple and pear farmers. The automatic machine learning solution H2O.AI can save time and effort compared to the conventional machine learning techniques such as scikit-learn, because it works automatically to find the best solution. As a result of this research, the following findings are obtained. First, apple farmers should increase their gross income to maximize their income, instead of reducing the cost of growing apples. In particular, apple farmers mainly have to increase production in order to obtain more gross income. As a second-best option, apple farmers should decrease labor and other costs. Second, pear farmers also should increase their gross income to maximize their income but they have to increase the price of pears rather than increasing the production of pears. As a second-best option, pear farmers can decrease labor and other costs.

• Proceedings of the KAIS Fall Conference
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• 2001.11a
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• pp.122-125
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• 2001

This paper presents the usage of software package APM WinMachine for design of equipment and buildings far agricultural industries. APM WinMachine is used for engineering analysis and design of parts of machines for industry and civil engineering. In process of machine design strength calculation is reuired for optimum design. With the help of APM WinMachine software strength calculations can be done quickly and correctly. In this paper a successful case of application of APM WinMachine for design of agricultural machine is introduced.

• Journal of Agricultural Extension & Community Development
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• v.3 no.1
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• pp.1-16
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• 1996

The purpose of this study was to interpret the transformation of Cooperative Groups for Agricultural Production(CGAP) with the change of the Agricultural Productive Force. The specific objectives were; 1) To investigate the change of agricultural labour-power, 2) To investigate the change of agricultural mechanization and arable land, 3) To interpret the transformation and content of CLAP. The population of farmhouseholds has decreased continuously since the late 1960s. Especially, with the move-outs of youth ages of twenties to forties, the condition of agricultural labour-power has been more serious. The processing of agricultural mechanization was a small scale step in the 1970s, but after the 1980s there was a spread of middle-large machines. However the usage rate of agricultural machines was constrained by the bad conditions of arable land. From the 1970s to now, the CGAP have bean processed by many kinds of patterns. In the 1970s, the lack of labour-power caused the creation of the Co-Working Team. After the late of 1970s, the wage of agricultural employees was raised, because the working population of agriculture was cut down. Also, the induction of agricultural machine was promoted. As a result, in the 1980s, the Machine-Using Team occurred due to these conditions of agricultural productive force. In the late of 1980s, the population decreased more rapidly, and the use of large machines were spread. Than farmhouseholds laking labour-power gave a trust to other farmhouseholds and Teams which had machines. In 1990, Given-Trust Cooperations were enacted by law, and in order to overcome the lack of labour-power, and solve the problem of the successors of agriculture, Cooperative Organizations were also enacted by law. Finally, in Korea from the 1970s to now, as the agricultural productive force has barn changed, the Co-Working Team was transformed into the Machine-Using Team, and the Machine-Using Team was transformed into the Given-Trust Cooperation, and the Cooperative Organization.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.31-40
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• 2018

We designed and implemented the attachable smart adapter for the general management of each company's agricultural machine regardless of whether it is equipped with a CAN (Controller Area Network) module. The smart adapter consists of a main board (Raspberry Pi3B), which operates agricultural machine's management software in Linux environment, and a self-developed interface board for power adjustment and status sensing. For the status monitoring, a sensing interface using a serial input was defined between the smart adapter and the sensors of the agricultural machine, and the state diagram of the agricultural machine was defined for diagnosis. We made a panel to simulate the sensors of the agricultural machine using the switch's on/off contact point, and confirmed the status monitoring and diagnostic functions by inputting each state of the farm machinery from the simulator panel.

• Journal of Biosystems Engineering
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• v.42 no.3
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• pp.227-233
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• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.02a
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• pp.107-113
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• 2000

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.446-459
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• 2000

This study was carried out in order to find out an effective machinery utilization strategy by conducting a survey on utilization and maintenance of agricultural machinery. The survey showed that the no. of utilization hours for power tiller in a year was 190.2hrs, 208.6hrs for tractor, 59.1hrs for rice transplanter, 74.0 hrs for combine, 44.6 cultivator and 254.4hrs for 4.4hrs for grain dryer. The period covered the time the machine was until it became unserviceable. The results are as follows: 10.0yrs for power tiller, 7.5yrs for tractor, 7.4yrs for rice transplanter and 5.4yrs for combine. This indicate that the actual period of use for power tiller and rice transplanter was longer than the expected period of duration years so there is a need for adjustment. The factors considered by the farmers for purchasing agricultural machine were: farm size(32%), machine operation (26.0%), performance(l4.0%) and post or after sales service(12.6%), according to the survey. It showed that repair cost rate in a year was classified into major agricultural machine; 4.8% for combine; 3.9% for tractor; 3.5% for rice transplanter; 2.0% for power tiller; 1.6% for grain dryer; and 1.2% for cultivator. The reasons for poor maintenance were insufficient after sales service(25%) and difficulty in buying parts(75%) because of the unavailability of parts in local shops(55%), imported models(30%) and outmoded model(15%).

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.131-140
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• 2015

This paper presents overall procedure by experimental study in order to deriving design factors of snow removal machine on roof of multi-span greenhouse. For the purpose of the testing, the scale model of the machine was made in the form to drive above the monorail. The test was performed in order to calculating friction coefficient of the machine and shear coefficient between sliced horizontal section of snow at constant temperature and humidity room in National Academic of Agricultural Science. As a result of the laboratory test, shear coefficient between sliced horizontal section of snow were calculated 1.60~2.37. Further investigation, we will study to derive the relationship between the real and scaled model through the field test.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.319-326
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• 2021

To improve the driving performance and work efficiency of the multi-purpose agricultural robot, this paper conducted a study on the turning and steering characteristics of the robot platform according to the characteristics of the working machine coupled to the multi-purpose agricultural robot considering the agricultural environment. First, the size and characteristics of the developed multi-purpose agricultural robot platform and working machine, and the targeted field farming work environment are analyzed. And based on this analysis, the problems that arise in multi-purpose robots with conventional turning methods are quantitatively presented. And to overcome this problem, an improved turning and steering method for multi-purpose agricultural robots is proposed considering the characteristics of various workstations and the agricultural working environment. Finally, by applying the proposed method, the turning characteristics of the multi-purpose agricultural robot according to the working machine are analyzed and the effectiveness of the proposed method is verified.