• International journal of advanced smart convergence
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• 제12권3호
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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.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2002년도 학술발표회 발표논문집
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• pp.401-404
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• 2002

Two irrigation districts, Maekok and Byungjum 1 which are irrigated with polluted stream flow, and one control district Kichun, that is supplied from a reservoir complying with the water quality standard are selected for water quality monitoring to identify the effects of polluted irrigation on crop yields, environments, and health hazards for farmers. The water quality at Maekok and Byungjum 1 districts are worse than the control district, and continuous water quality monitoring are needed for the wastewater reuse for agriculture.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1033-1035
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• 2003

This study attempts to analyze the potential of global scale NDVI data archive to monitor regional scale droughts. Ten-days maximum value NDVI composite data of the northeast Asia region were acquired for the growing seasons from 1993 to 2003. Two NDVI-derived drought indices (SVI, VCI), reported from previous studies, were applied to the study area. Although the SVI and VCI are mainly developed for monitoring the drought condition at the agriculture crop and grasslands, it turned out that they were also effective to reveal the drought condition over the temperate mixed forest. The drought symptom lasts at least one or two months even after the normal raining begins.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.89-96
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• 2021

The goal of this study is to develop experiment monitoring system of agriculture fields using ZigBee wireless modules. Soil moisture, ambient temperature, atmospheric pressure and intensity of sunlight are the most important factorsto grow a wheat crop and other vegetables. In orderto monitorthe factorssoil moisture (YL69), air pressure (BMP180), temperature (DS18B20), photoresistor were used for sensing environment data. The TI CC2530 RF SoC chip was used in the system. ZigBee modules were connected to star topology. ZigBee modules send data wirelessly to a data center. This data can be displayed and analyzed on the main monitoring program as needed also sent to the client mobile. Characteristics of the sensors were determined by experiment results.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.310-312
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• 2015

Small Unmanned Aerial Systems (SUAS) have been gaining a special attention in the U.S. recently because it is capable of getting aerial footages conveniently and cost effectively, but also because of its potential threat to the safety of our society. Regarding the benefits, one can easily find successful cases. For example, remote controlled or pre-programmed unmanned aircraft help ranch owners monitor their livestocks or crop harvesting status cost-effectively without having to hire human pilots. The professionals in the construction industry also acknowledge the benefits they could gain from using SUAS. Some firms already use a small unmanned aircraft for monitoring their construction activities, which may help project managers figure out construction progress, resolve disputes in real time, and make proactive decisions for quality control. However, there are many technical challenges that my hinder the use of small unmanned aircraft in the construction industry. This paper explores opportunities and challenges in using unmanned aircraft to monitor concrete cracks on the surface of containment building in the nuclear power plant.

• 한국환경농학회:학술대회논문집
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• 한국환경농학회 2009년도 정기총회 및 국제심포지엄
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• pp.93-115
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• 2009

There are growing public concerns over crop and food safeties due to the elevated levels of heavy metals grown in contaminated soil. Heavy metals are classified as the chemical harmful risks for crop and food safety. With implementation of GAP, crop safety is controlled by many regulatory options for soil, irrigation water and fertilizers. Any attempt to retard the metal uptake by crops may be the best protocol to secure crop and food safety. This article reviews the management strategies for heavy metals in view of crop safety in Korea and demonstrates results from the field experiments to retard metal translocation from soil to crops by using chemical amendments and soil layer management methods. Major source of soil pollution by heavy metals has been related with mining activities. Risk assessment revealed that rice consumption and groundwater ingestion in the abandoned mining areas were the major exposure pathways for metals to human and the heavy metal showed the toxic effects on human health. Chemical amendments such as lime and slag retarded Cd uptake by rice (Oryza sativa L.) by increasing soil pH, lowering the phytoavailable Cd concentration in soil solution, immobilizing Cd in soil and converting the available Cd fractions into non-available fractions. The soil layer management methods decreased the Cd uptake by 76% and Pb by 60%. Either reversing the surface layer with subsurface layer or immobilization of metals with layer mixing with lime was considered to be the practical option for the in-situ remediation of the contaminated paddy soils. Combination of chemical soil amendments and layer management methods was efficient to retard the metal bioavailability and thus to secure crop safety for heavy metals. This protocol seems to be cheap, relatively easy to practice and practical in the agricultural fields. However, a long term monitoring work should be followed to verify the efficiency of this protocol.

• 대한원격탐사학회지
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• 제40권1호
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• 한국환경농학회지
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• 제30권4호
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• pp.432-439
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• 2011

BACKGROUND: An official analytical method was developed to determine etofenprox residues in agricultural commodities using high-performance liquid chromatography (HPLC). METHODS AND RESULTS: The etofenprox residue was extracted with acetone from representative samples of five raw products which comprised rice grain, apple, mandarin, cabbage, and soybean. The extract was then serially purified by liquid-liquid partition and Florisil column chromatography. For rice and soybean samples, acetonitrile/n-hexane partition was additionally coupled to remove nonpolar lipids. Reversed phase HPLC using an octadecylsilyl column was successfully applied to separate etofenprox from co-extractives. Intact etofenprox was sensitively detected by ultraviolet absorption at 225 nm. Recovery experiment at the quantitation limit validated that the proposed method could apparently determine the etofenprox residue at 0.02 mg/kg. Mean recoveries from five crop samples fortified at three levels in triplicate were in the range of 93.6~106.4%. Relative standard deviations of the analytical method were all less than 10%, irrespective of crop types. A selected-ion monitoring LC/mass spectrometry with positive atmospheric-pressure chemical ionization was also provided to confirm the suspected residue. CONCLUSION(s): The proposed method is simple, rapid and sensitive enough to be employed in routine inspection or monitoring of agricultural products for the etofenprox residue.

• 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.

• 농업과학연구
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• 제48권4호
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• pp.1051-1065
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• 2021

Recent times have seen sustained increases in genetically modified (GM) crops not only for cultivation but also for the utility of food and feed worldwide. Domestically, commercial planting and the accidental or unintentional release of living modified (LM) crops into the environment are not approved. Many detection methods had been devised in an effort to realize effective management of the safety of agricultural genetic resources. In order to develop event-specific polymerase chain reaction (PCR) markers for LM crops, we analyzed the genetic information of LM crops. Genetic components introduced into crops are of key importance to provide a basis for the development of detection methods for LM crops. To this end, a total of 18 varieties from four major LM crop species (maize, canola, cotton, and soybeans) were subjected to an analysis. The genetic components included introduced genes, promoters, terminators and selection markers. Thus, if proper monitoring techniques and single or multiplex PCR strategies that rely on selection markers can be established, such an accomplishment can be regarded as a feasible solution for the safe management of staple crop resources.