• Journal of Biosystems Engineering
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• v.8 no.1
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• pp.17-29
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• 1983

The milling process is considered as causing one of the greatest grain losses among all the processes in rice post-production. Major source of grain losses in the rice milling is considered as the whitening process. This study was attempted to develop an abrasive-type whitener, the whitening chamber of which being supplied by jet-air evenly and continuously. To investigate the milling performance by the new whitener, three kind of emery-stone grit(#36, #41, and #46), and three levels of rotational speed of emery stone roller (950, 1070, and 1200 rpm) were tested. The jet-air abrasive-type whitener was also compared with the conventional abrasive-type having no jet-air blower in terms of their milling performance. In addition, the effect of different combinations of sequential uses of the abrasive- and friction-type whiteners on the milling performance was also experimentally evaluated. The results of this study are summarized as follows; 1. In general, the whitening system combined with the abrasive type whitener with jet-air supply, which was newly designed, and the existing jet-air friction type whiteners produces more milled- and head-rice by about 0.3% points and 2.8% points, respectively than the system combined with the existing abrasive type without the jet-air supply under the same operational conditions. The former also consumed less electricity by 0.024 KwH per 100kg of milled rice production and gave more milling capacity by about 35 kg/hr. As compared with the conventional whitening system consisting of jet-air friction type whiteners only, the former yielded more milled- and head-rice by 1.5% points and 4.4% points, respectively. 2. The abrasive roller having 46 grit emery was better than the roller having 36 grit in aspects of milling performance and machine efficiency, in general. 3. With regard to the effect of combination method of abrasive type and friction type whiteners, one pass in abrasive type plus three passes in friction type gave better milling performance and energy efficiency than the two passes in abrasive type plus two passes in friction type regardless of the designs of the emery stone rollers. 4. The increase in rotational speed of the emery stone roller from 950 rpm to 1200 rpm presented negative effects on milled and head-rice yields and machine efficiency, but slightly positive effect on milling capacity.

• Journal of Korea Water Resources Association
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• v.53 no.6
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• pp.395-408
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• 2020

This paper aims to develop and apply three different machine learning approaches (i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and wavelet-based neural networks (WNN)) combined with an evolutionary optimization algorithm and the k-fold cross validation for multi-step (days) streamflow forecasting at the catchment located in Algeria, North Africa. The ANN and ANFIS models yielded similar performances, based on four different statistical indices (i.e., root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and peak flow criteria (PFC)) for training and testing phases. The values of RMSE and PFC for the WNN model (e.g., RMSE = 8.590 ㎥/sec, PFC = 0.252 for (t+1) day, testing phase) were lower than those of ANN (e.g., RMSE = 19.120 ㎥/sec, PFC = 0.446 for (t+1) day, testing phase) and ANFIS (e.g., RMSE = 18.520 ㎥/sec, PFC = 0.444 for (t+1) day, testing phase) models, while the values of NSE and R for WNN model were higher than those of ANNs and ANFIS models. Therefore, the new approach can be a robust tool for multi-step (days) streamflow forecasting in the Seybous River, Algeria.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.1-11
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• 1987

This study was conducted to evaluate the effect of several fungicides and growth regulators on rice seedling growth and damping-off in seedling boxes for machine transplanting. Fungicide treated plots were better seedling growth, shoot regrowth, rooting ability, change of moisture content than those of nontreated plot. Metalaxyl application of Samgangbyeo and SF8002 application of Nagdongdyeo apparently increased plant height, length of the third leaf and fourth leaf. And metaiaxyl application highly increased dry weight. Fungicide treated plots were highly effective in reducing the incidence of damping-off. Benzyladenine application of Samgangbye and $GA_3$ application of Nagdongbyeo apprently increased plant height. But ABA application highly decreased plant height. ABA application and aCE application resulted in highly increased rooting ability. Fungicide and $GA_3$ treated plots, Metalaxyl and growth regulator treated plots resulted in highly increased plant height. I soprothiolane and growth regulator treated plots resulted in decreased plant height. Dachigaren and lAA treated plot apprently increased dry weight and shoot dry weight/plant height. Fungicide and growth regulator treated plots were highly effective in reducing the incidence of damping-off.

• Current Research on Agriculture and Life Sciences
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• v.2
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• pp.1-8
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• 1984

This study was conducted to evaluate the effect of various fertilizers and their application methods on pH in seedbed soil and seedling vigor of rice cultivars, "Nagdong" and" Samgang", for machine transplanting. The application of amnonium sulfate as nitrogen sources showed higher plant height and dry weight of seedlings than those of the urea treated plot. Seedling rot was highly occured in the basal application of urea than that of split treatment of urea, while the lower seedling rot was observed in ammonium sulfate treated plot than that of treatment. Regardless of the application methods, the higher rooting ability was observed in anmonium sulfate treatment than that of urea. Ammonium sulfate and superphosphate as nitrogen and phosphate sources, respectively, showed lower pH level than that of urea and fused phosphate treated plots. The use of ammonium sulfate and super phosphate as nitrogen and phosphate sources, respectively, seems to be effective to maintain the optimum pH level and to rear the healthy seedling, than that of urea or fused phosphate application.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.33 no.3
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• pp.281-286
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• 1988

These studies were conducted to find out the polishing methods that improve yield and quality of the polished barley. Four varieties of hulled barley, Dongbori 1. Bunong, Kangbori and Suwon 182 which were produced in Suwon, Korea in 1979, were subjected to this experiment. The polishing machine, manufactured by Satake Co, was used as test mill. Increasing the roller speed of polishing machine causes more polished barley in a unit period. The speed influenced more in length than thickness or width of grain. Therefore the shape of grain became bold type as the speed increased. The optimum roller speed was 1,300rpm in ideal shape of polished barley. The lowest hardness was observed in the husk layer and the hardness was found in the decreasing order of the aleurone, testa, peri carp and the endosperm layer. The thickness of bran layer, weight of 1,000 kernel and hardness of polished barley were greatly different according to barley varieties. Also the length, thickness, width and the ratio of length to width of barley grain were significantly different in barley varieties. The ratio of length to width of the polished barley was 1.59 in Suwon 182, 1.53 in Bunong, 1.51 in Kangbori and 1.26 in Dongbori 1.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Journal of Bio-Environment Control
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• v.33 no.1
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• pp.63-70
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• 2024

This study explored computer vision methods using the OpenCV open-source library to characterize the phenotypes of various horticultural crops. In the case of tomatoes, image color was examined to assess ripeness, while support vector machine (SVM) and histogram of oriented gradients (HOG) methods effectively identified ripe tomatoes. For sweet pepper, we visualized the color distribution and used the Gaussian mixture model for clustering to analyze its post-harvest color characteristics. For the quality assessment of netted melons, the LAB (lightness, a, b) color space, binary images, and depth mapping were used to measure the net patterns of the melon. In addition, a combination of depth and color data proved successful in identifying flowers of different sizes and distances in cucumber greenhouses. This study highlights the effectiveness of these computer vision strategies in monitoring the growth and development, ripening, and quality assessment of fruits and vegetables. For broader applications in agriculture, future researchers and developers should enhance these techniques with plant physiological indicators to promote their adoption in both research and practical agricultural settings.