• Current Research on Agriculture and Life Sciences
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• v.15
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• pp.115-122
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• 1997

Grafting is an important skill for the stable supply and production of high quality. However, the shortage of skillful labor has become great difficulty for a mass production of grafting-seedling. In this study, a suitable mechanism for a grafting machine was developed. The following summarize the results of this study: 1. An insertion method was selected for mechanism of the grafting machine without bonding agent, clip, pin. This insertion-grafting method can be applicable to general vegetables and a mass production system. In addition to, this method is suitable for developing the grafting mechanism. 2. Growing point was removed while remaining both cotyledons on rootstock. The productivity of this system was five fold greater than the one of an experienced labor. 3. The rootstock processing was placed on left and scion processing unit was placed on right of the system, then processed rootstock and scion graft by rotating $180^{\circ}$. 4. The efficiency tests on mechanical grafting rate showed 98%.

• Journal of Biosystems Engineering
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• v.39 no.1
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• pp.1-10
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• 2014

Purpose: This research was conducted to evaluate feasibility of a crank-type walking cultivators for weeding in furrowed upland. Methods: A walking cultivator developed by RDA was selected and evaluated with its working speed (S), cultivation depth (CD) and weeding performance (WP). The evaluation was performed in upland field on July and August, 2012. Also kinematic analysis of the machine was performed to draw out design improvements. Results: S in flat, uphill and downhill were about 0.11 m $s^{-1}$, 0.11 m $s^{-1}$, and 0.13 m $s^{-1}$ respectively. It was found that S had a low relevance with user conditions. The CD was 35 ~ 40 mm which was satisfied with the RDA guide for weeding machine. A wide variation was observed in values of WP depending on the growth stages of weeds and field conditions. The cultivator showed low performance in eliminating the well-grown weeds. Kinematic simulation revealed that high forward speed caused a high ratio of un-weeded area. Conclusions: The weeding performance of the cultivator was satisfactory for weeds in early growth stage but it showed difficulties in handling on up-slope and in entering up-land. Specifically, the weight of the cultivator was judged as overweight for female workers. The crank-hoe type cultivator was judged as unsuitable for small walking type machine due to weight of the four-bar linkage system. Kinematic analysis revealed that the ratio of crank speed to the ground speed must be 850 rpm s $m^{-1}$ (255 rpm based on 0.3 m $s^{-1}$) or greater to avoid uncultivated area. Selection of forward speed is a decisive factor in designing the weeding cultivator.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of Biosystems Engineering
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• v.34 no.6
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• pp.425-433
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• 2009

The developed final shattering machine for labor-saving mechanization of shattering of sesame consisted of input part, shattering part, re-shattering part for unshattered pod and pneumatic sorter. The bundle of sesame was held as upside down and fed into the machine continuously. Then, the fed bundle of sesame was shattered by side shock and agitation. The performance of shattering for the sun dried bundle of sesame of conventional manual work and final shattering machine was compared. Since the shattering ratio measured by the final machine was 97.2% at the first operation, in case of fully dried sesame by drying stand, the harvest of sesame can be completed by only one time shattering operation. The work hour per area of 10 a for the mechanical work and the manual work were 0.3 hour and 13.9 hour, respectively. The total shattering ratio of the final machine with vertical feedings of bundle of sesames was 97.2%.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.393-403
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• 1996

The tractors and field crop machinery used in North American are produced by a mature industry. Recent technological innovations in include machinery for spatially -variable crop production , electronics for machine control and tractor-implement communications, low-emission and alternative fuel engines , flexible power transmission, and larger and more sophisticated equipment . Trends and issues are discussed.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.953-956
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• 1996

The main regularities of the process of strain of the leafstalk boimass of the annual and parennial sown grasses (hard phase) with the simulataneous filtration of the vegetable juice (liquid phase) in the working members of the uninterrupted action(screw press) and the periodic action (Briqueting stamp press) were established . The engineering methods of calculation of the basic constructive -technological parameter of the pres equipment of the given types were worked out.

• Proceedings of the Korean Society for Bio-Environment Control Conference
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• 2003.10a
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• pp.138-142
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• 2003

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.