• Journal of Biosystems Engineering
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• 제35권3호
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• pp.175-181
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• 2010

This study compared moisture content measured by moisture meter of microwave resistance type(MMMRT) and standard moisture content of paddy, and developed the correction equation using linear and curvilinear regression analysis, and to explore its significance test. The correction factor according to the range of moisture content was developed to improve the measurement precision of MMMRT. The results were as followings. The coefficients of determination of correction equation by linear and curvilinear regression analysis with comparing the MMMRT and standard moisture content were 0.946 and 0.968, respectively. The moisture content error of MMMRT and standard moisture content measured after the MMMRT were corrected by moisture content rate of every 5% using the correction equation by curvilinear regression analysis appeared with 0~0.5% and 0.9~1.8% respectively in the moisture content range of 15~20% and 20~25%.

• Agricultural and Biosystems Engineering
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• 제5권1호
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• pp.10-20
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• 2004

Precision agriculture aims to minimize costs and environmental damage caused by agriculture and to maximize crop yield and profitability, based on information collected at within-field locations. In this process, quantification of soil physical properties, including soil strength, would be useful. To quantify and manage variability in soil strength, there is need for a strength sensor that can take measurements continuously while traveling across the field. In this paper, preliminary analyses were conducted using two datasets available with current technology, (1) cone penetrometer readings collected at different compaction levels and for different soil textures and (2) tillage draft (TD) collected from an entire field. The objective was to provide information useful for design of an on-the-go soil strength profile sensor and for interpretation of sensor test results. Analysis of cone index (CI) profiles led to the selection of a 0.5-m design sensing depth, 10-MPa maximum expected soil strength, and 0.1-MPa sensing resolution. Compaction level, depth, texture, and water content of the soil all affected CI. The effects of these interacting factors on data obtained with the soil strength sensor should be investigated through experiments. Spatial analyses of CI and TD indicated that the on-the-go soil strength sensor should acquire high spatial-resolution, high-frequency ($\ge$ 4 Hz) measurements to capture within-field spatial variability.

• Asian-Australasian Journal of Animal Sciences
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• 제28권4호
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• pp.592-598
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• 2015

Stress adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

• 항공우주기술
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• 제10권1호
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• pp.149-155
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• 2011

세계적 기후온난화와 이상기온현상으로 최근 급변하는 농업환경에 대응하기 위해서는 농작물 작황관리 및 예측시스템의 과학화를 통한 정부차원의 대처능력 개선이 시급하다. 농업분야에서 위성정보의 활용은 고해상도 광학 및 레이더 영상의 상용화와 더불어 정밀농업이라는 새로운 가능성을 열어주고 있다. 본 연구에서는 최근 농업분야에서 주목을 받고 있는 RapidEye 위성영상을 사용하여 우리나라 농촌지역의 토지이용유형별 분광식생지수의 시계열 특성을 살펴보았다. 식생과 비식생지역 간에 뚜렷한 시계열 변화양상이 나타났으며, 식생지역 내에서도 산림 수종별, 논 그룹별로 식생지수의 시계열 변화에 차이가 관찰되었다.

• Environmental Engineering Research
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• 제22권2호
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• 농업생명과학연구
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• 제46권6호
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• pp.165-171
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• 2012

As a preceding study for investigating the influence of sound wave stimulus on Arabidopsis thaliana metabolomics, the polar secondary metabolomes of the plant were determined using high performance liquid chromatography coupled with tandem mass spectrometry. A total of 10 polar secondary metabolomes were characterized and quantified. Among them, 4 metabolomes, p-coumaroylagmatine isomer (7 and 8), p-coumaroylagmatine isomer (9 and 10) were identified in the plant for the first time. The validation was conducted in terms of linearity, recovery, precision, limit of detection (LOD) and limit of quantification (LOQ). The validated method was applied to the simultaneous quantification of the 10 polar secondary metabolomes.

• 한국지하수토양환경학회지:지하수토양환경
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• 제26권6호
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• pp.157-164
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• 2021

Smart agriculture requires sensing systems which are fundamental for precision agriculture. Adequate and appropriate water and nutrient supply not only improves crop productivity but also benefit to environment. However, there is no available soil sensor to continuously monitor nutrient status in soil. Electrical conductivity (EC) of soil is affected by ion contents in soil and can be used to evaluate nutrient contents in soil. Comparison of various commercial EC sensors showed similar water content and EC values at water content less than 20%. Soil EC values measured by sensors decreased with decreasing soil water content and linearly correlated with soil water content. EC values measured by soil sensor were highly correlated with water soluble nutrient contents such as Ca, K, Mg and N in soil indicating that the soil EC sensor can be used for monitoring changes in plant available nutrients in soil.

• 생약학회지
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• 제50권2호
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• pp.133-140
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• 2019

In our previous study, we found uracil, adenosine, protocatechuic acid, syringin (eleutheroside B) and scoparone (6, 7-dimethoxycoumarin) in the Oplopanax elatus Nakai Stem. High-performance liquid chromatography (HPLC) -UV was used to quality and quantify the internal marker compounds in the O. elatus extract after validation of method with linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy and precision. The specificity assessment visually confirmed that the substance was detected without the introduction of other substances. The established method showed high linearity of the calibration curve and coefficient of correlation ($R^2$) of over the 0.999. HPLC was reported as five standard compounds equivalent using the following linear equation based on the calibration curve. The accuracy of measurement was 84.34 ~ 119.74% and the relative standard deviation (RSD) value was 0.28 ~ 1.60%. In addition, our established method showed high repeatability. The RSD value was 1.10 ~ 6.81%. So, we found the amount of the internal marker compounds in the O. elatus extract. These results demonstrated that can be used to quality evaluation of the O. elatus.

• 스마트미디어저널
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• 제13권2호
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• pp.16-22
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• 2024

Greenhouse provides opportunities to have big yield effectively and efficiently. However, many resources are required, such as fertigation, a kind of solution of nutrient. Resources supply is essential to cultivate crops. Inadequate supply will hinder plant growth whereas the surplus results in waste. In this paper, we are especially interested in the fertigation supply. Further, excess fertigation leads to drainage which is difficult to purify and threatens the environment. To address this challenge, we aim to predict the desired amount of fertigation. To achieve this objective, we first establish a prototype to record the climate conditions inside a rose greenhouse using Internet of Things sensors. Simultaneously, the desired fertigation amount is obtained with the help of weight scale and historical data of fertigation supply and drainage. Second, a method is proposed to predict the desired fertigation by taking the sensors' data as input, with a time-series model. Extensive experimental results suggest the potential of our objective and method. To be specific, our method achieves an average MAE 0.032 in the validation datasets.

• 산업경영시스템학회지
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• 제43권1호
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.