• Korean Journal of Soil Science and Fertilizer
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• v.47 no.5
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• pp.313-318
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• 2014

The remote monitoring system of water salinity was assessed in Wando reclaimed land lake during a farming season in 2009. Increasing of water salinity in this lake used to bring about salt damage on rice plant occasionally. At the early stage of the rice growing period, rice growth was not damaged due to enough rainfall with more than 120 mm from the mid-May to the first ten days of June. Data collection using on-site water salinity measuring sensors every 2 hours and real-time transmission in system were carried out for the experiment. We compared the transmitted values from the sensor system with water sample values collected and analyzed by a local technical office. Salt concentrations measured by sensor in real-time monitoring system were available data. The regression equation between rainfall and water salinity was presented as (water salinity after rainfall) = $0.621{\times}$(water salinity before rainfall)${\times}exp(-0.0139{\times}rainfall)$, ($r^2=0.579$, p<0.01). It is suggested that the system is useful for stable farming in the area where farmer use water in reclaimed lakes as an irrigation source.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.21-28
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• 2017

The main goal of this paper is to assess application of UAV (Unmanned Aerial Vehicle) remote sensing and GIS based images in detection and measuring of rice field drought area in South Korea. Drought is recurring feature of the climatic events, which often hit South Korea, bringing significant water shortages, local economic losses and adverse social consequences. This paper describes the assesment of the near-realtime drought damage monitoring and reporting system for the agricultural drought region. The system is being developed using drought-related vegetation characteristics, which are derived from UAV remote sensing data. The study area is $3.07km^2$ of Wonbuk-myeon, Taean-gun, Chungnam in South Korea. UAV images were acquired three times from July 4 to October 29, 2015. Three images of the same test site have been analysed by object-based image classification technique. Drought damaged paddy rices reached $754,362m^2$, which is 47.1 %. The NongHyeop Agricultural Damage Insurance accepted agricultural land of 4.6 % ($34,932m^2$). For paddy rices by UAV investigation, the drought monitoring and crop productivity was effective in improving drought assessment method.

• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.6
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• pp.33-43
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• 2009

This study analyzed data on the pore water pressure, the ground water level, the horizontal displacement and the resistivity monitoring from instrument system, which is established to evaluate the safety in reservoirs. The pore water pressure in the embankment ranged from $0.035{\sim}1.116kg/cm^2$. The seepage that piping showed, as well as the leakage from the reservoirs are acceptable for the safety management of the reservoir. The maximum horizontal displacement and direction analyzed from the measured inclinometer data gives us very effective information to evaluate the safety in reservoirs. The resistivity monitoring technique, which is obtained on the reservoir crest, is an efficient tool to detect leakage zone. The safety index (SI) was predicted by the resistivity monitoring, and was evaluated to have a safety level of 0.8-1.0 at all reservoirs. Safety evaluations of reservoir through instrument systems are effective when studying the embankment, when the results of the instrument system have been analyzed compositively.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.293-300
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• 2000

Automated greenhouse production system often require crop growth monitoring involving accurate quantification of plant physiological properties. Conventional methods are usually burdensome, inaccurate, and harmful to crops. A thermal image analysis system can accomplish rapid and accurate measurements of physiological-property changes of stressed crops. In this research a thermal imaging system was used to measure the leaf-temperature changes of several crops according to nutrient stresses. Thermal images were obtained from lettuce, cucumber, and pepper plants. Plants were placed in growth chamber to provide relatively constant growth environment. Results showed that there were significant differences in the temperature of stressed plants and non-stressed plants. In a case of the both N deficiency and excess, the leaf temperatures of cucumber were $2^{\circ}C$ lower than controlled temperature. The leaf temperature of cucumber was $2^{\circ}C$ lower than controlled temperature only when it was under N excess stress. For the potassium deficiency or excess stress, the leaf temperaures of cucumber and hot pepper were $2^{\circ}C$ lower than controls, respectively. The phosphorous deficiency stress dropped the leaf temperatures of cucumber and hot pepper $2^{\circ}C$ and $1.5^{\circ}C$ below than controls. However, the leaf temperature of lettuce did not change. It was possible to detect the changes in leaf temperature by infrared thermography when subjected to nutrition stress. Since the changes in leaf temperatures were different each other for plants and kinds of stresses, however, it is necessary to add a nutrient measurement system to a plant-growth monitoring system using thermography.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.25-30
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• 1999

The effectiveness of many greenhouse environment control methodologies depends on the growth information of crops. Acquisition of the growth information of crops requires a non-invasive and continuous monitoring method. Crop growth monitoring system using digital imaging technique was developed to conduct non-destructive and intact plant growth analyses. The monitoring system automatically measures crop growth information sends an appropriate control signal to the nutrient solution supplying system. To develop the monitoring system, a linear model that explains the relationship between the fresh weight and the top projected leaf area of a lettuce plant was developed from an experiment. The monitoring system was evaluated buy successive lettuce growing experiments. Results of the experiments showed that the developed system could estimate the fresh weight of lettuce from a lettuce image by using the linear model and generate an EC control signal according to the lettuce growth stage.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.6
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• pp.163-171
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• 2015

This study aimed to establish a field observation system for monitoring tempo-spatially precise changes of groundwater level and to analyze the impact of rainfall and irrigation practices on groundwater changes in paddy regions. The monitoring system comprising of all nine groundwater observation wells and four ponding depth sensors was installed in a part of paddy regions benefited from Gosam reservoir, Ansung-si. The result of grundwater level change during the irrigation period in 2002 was averagely 0.51 m higher than that during the non-irrigation period. In particular between March before puddling and June after transplanting, there was maximum 1.23 m rise in groundwater level. On the other hand, concerning the change in ponding depth, groundwater level changed similarly, and hourly rainfall was revealed to have better correlation with 24-hour delayed hourly groundwater level than with the corresponding groundwater level. Eventually, this study could be referenced for further studies to set up a more comprehensive and sustainable monitoring system of groundwater conditions.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.117-125
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• 2003

Since the recorded information used for operation of a catchment modelling system contain errors that influence the calibration of catchment modelling system control parameter values, the accurate estimation of these parameters is difficult. Despite these influences, existing traditional calibration approaches focus only on achieving the best "curve fitting" between simulated and recorded data, and not on generic evaluation of control parameter values. This paper introduces an Early Stopping Technique which is aimed at avoiding the procedure of curve-fitting through monitoring improvements in the objective function used for assessing the optimal parameter set. Application of this approach to the calibration of SWMM (Storm Water Management Model) on the Centennial Park catchment in Sydney, Australia is outlined. outlined.

• Journal of Biosystems Engineering
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• v.17 no.2
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• pp.177-191
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• 1992

The MAFSAP(Monitoring and Forecasting System of Air Pollution) was developed to measure the weather and air pollution data automatically, then make them input to microcomputer and analyze them for monitoring and forecasting air pollution at all times. And the air pollution telemetering systems installed at Young-Dong Thermal Power Plant was analyzed and an ideal telemetering system utilizing MAFSAP was suggested.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.173-178
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• 2007

This research was conducted to develop and analyze a wireless monitoring system for judging if sick or dead layers (SDL) exist in multi-tier layers battery (MLB) by machine vision, and to evaluate the performance between a wired monitoring system and it. This study used the AP (Access Point), the RS-285 to RS-232 converter, RS-232 to Ethernet converter, PICBASIC board and upgraded lump image processing method to change wired monitoring system into wireless monitoring system. The system was tested at a pilot farm and farm layer house. Results showed that monitoring judgement success rate at a pilot farm on normal cage (without SDL) was 82.3% and that on abnormal cage (with SDL) was 87.5%, respectively. And communication performance test results showed at farm layer house was $700{\sim}900$ kbps while equipments operated. There were dropped slightly than performance of wired monitoring system, however, the quantity was too small to make a significant difference of performance of the controling system developed for wireless communication.