• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1150-1169
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• 2023

Edible insects are gaining popularity as a potential future food source because of their high protein content and efficient use of space. Black soldier fly larvae (BSFL) are noteworthy because they can be used as feed for various animals including reptiles, dogs, fish, chickens, and pigs. However, if the edible insect industry is to advance, we should use automation to reduce labor and increase production. Consequently, there is a growing demand for sensing technologies that can automate the evaluation of insect quality. This study used short-wave infrared (SWIR) hyperspectral imaging to predict the proximate composition of dried BSFL, including moisture, crude protein, crude fat, crude fiber, and crude ash content. The larvae were dried at various temperatures and times, and images were captured using an SWIR camera. A partial least-squares regression (PLSR) model was developed to predict the proximate content. The SWIR-based hyperspectral camera accurately predicted the proximate composition of BSFL from the best preprocessing model; moisture, crude protein, crude fat, crude fiber, and crude ash content were predicted with high accuracy, with R² values of 0.89 or more, and root mean square error of prediction values were within 2%. Among preprocessing methods, mean normalization and max normalization methods were effective in proximate prediction models. Therefore, SWIR-based hyperspectral cameras can be used to create automated quality management systems for BSFL.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.121-131
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• 2018

Climate change has caused changes in environmental factors that have a direct impact on agriculture such as temperature and precipitation. The meteorological disaster that has the greatest impact on agriculture is drought, and its forecasts are closely related to agricultural production and water supply. In the case of terrestrial data, the accuracy of the spatial map obtained by interpolating the each point data is lowered because it is based on the point observation. Therefore, acquisition of various meteorological data through satellite imagery can complement this terrestrial based drought monitoring. In this study, Evaporative Stress Index (ESI) was used as satellite data for drought determination. The ESI was developed by NASA and USDA, and is calculated through thermal observations of GOES satellites, MODIS, Landsat 5, 7 and 8. We will identify the difference between ESI and other satellite-based drought assessment indices (Vegetation Health Index, VHI, Leaf Area Index, LAI, Enhanced Vegetation Index, EVI), and use it to analyze the drought in South Korea, and examines the applicability of ESI as a new indicator of agricultural drought monitoring.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.63-71
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• 2010

In this paper, we propose the Agricultural Environment Monitoring System based on Sensor Network which can collect information of crop cultivation environment and monitor it in real-time by using various environment sensors. Existing wireless sensor nodes, based on the sensor network, require extra conversion/control module depending on the characteristics. To solve this problem, we developed an integrated sensor module which can integrate various kinds of sensors used to obtain the necessary information for the area under crop cultivation. In addition, we developed sensor networks monitoring system which is suitable for an integrated sensor module. To verify the operating status of the proposed system, an integrated sensor node is installed in the test environment so that it can sense information of the environment and monitor it in real-time.

• The Korean Journal of Pesticide Science
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• v.20 no.1
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• pp.23-29
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• 2016

This study was carried out to survey residual characteristics of pesticide in fresh ginsengs collected from 45 markets at 15 regions in Korea using multiresidue analysis with a GC-MS/MS and an LC-MS/MS. After residue analysis was performed, the pesticides detected from ginsengs were quantitated using their analytical methods validated by recovery tests with a GC-ECD/NPD. As a results of analysis of pesticide residue, cypermethrin, fenitrothion, fludioxonil, thifluzamide, and tolclofos-methyl were detected from 16 samples among 45 samples in total, indicating detection rate was 35.6%. Tolclofos-methyl was found to be highest in detection frequency in ginseng. Fenitrothion that has not established maximum residue limit and pre-harvest interval for ginseng was detected. The amounts of all pesticides detected were less than their MRLs. Ratios of estimated daily intakes to acceptable daily intakes of the detected pesticides in ginseng were found to be from 0.03 to 16.67%.

• The Korean Journal of Pesticide Science
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• v.14 no.1
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• pp.1-9
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• 2010

In order to survey residual characteristics of pesticides in the agricultural products selling at markets and to assess their safety, a total of 120 agricultural products were collected from the wholesale and traditional markets in Cheongju and analyzed the pesticide residues in them by multiresidue analysis method using GLC, HPLC and GC-MSD. Three pesticides, procymidone, penconazole, and tetraconazole, were detected from 4 samples such as onion, leek, tomato, and green pepper. Fungicide penconazole was detected from the onion collected from wholesale market. Fungicide procymidone was detected from leek and tomato and fungicide tetraconazole was detected from green pepper. Pesticide residues were detected from 3.3% of the total samples. The estimated daily intakes (EDIs) of the pesticides detected were less than 0.1% of their acceptable daily intakes (ADIs), representing that residue levels of the pesticides detected were evaluated as safe.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.75-83
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• 2017

Many parts of problems in livestock industry today are associated with organic dust. Endotoxin and toxic gasses on the surface of dust and dust itself can cause aesthetic displeasure and respiratory disease. It also reduces livestock productivity by suppressing immunity of animals and carrying microbes causing animal disease. However, dust level of cattle farm was rarely reported in Korea, and regulation for cattle farm worker does not exist. In this paper, dust concentration and environmental condition were regularly monitored in a commercial Korean native cattle farm. The measurement was conducted according to location and working activities. From the measurement, distribution of dust concentration was affected by wind environment, as the result of natural ventilation. TMR mixer was a major source of dust in target cattle house. The maximum inhalable dust concentration was 637.8 times higher than exposure limit as feed dropped into the TMR mixer. It was expected that dust generation could be affected by particle size and drop height of feed. This study suggests potential risk of dust in cattle farm, and necessity for latter study. Effect of aerodynamic condition and TMR processing should be investigated for dust reduction study.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.323-329
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• 2011

BACKGROUND: The conazoles, difenoconazole, diniconazole, hexaconazole, penconazole and tetraconazole are a large class of synthetic fungicides used extensively for foliage and seed treatments in agricultural crops. The extensive use of conazoles has brought concerns on the potentiality of environmental contamination and toxicity. Thus studies on the development of methods for monitoring the conazoles are required. METHODS AND RESULTS: A modified quick, easy, effective, rugged and safe (QuEChERS) method was involved in sample preparation. Quadrapole time of flight mass spectrometer (Q-TOF MS) in electron spray ionization (ESI) mode was employed to determine conazoles in garlic samples. The limit of detection (LOD) and limit of quantification (LOQ) of conazoles by Q-TOF-MS ranged from 0.001 to 0.002 mg/L and 0.002 to 0.005 mg/L, respectively. Q-TOF-MS analysis exhibited less than 2.6 ppm error of accurate mass measurements for the detection of conazoles spiked at 0.05 mg/L in garlic matrix. Recovery values of conazoles fortified in garlic samples at 0.02, 0.05 and 0.1 mg/L were between 79.2 and 106.2% with a maximum 11.8% of standard deviation. No detectable conazoles were found in the domestic market samples by using the Q-TOF-MS method. CONCLUSION(s): High degree of confirmation for conazoles by accurate mass measurements demonstrated that Q-TOF-MS analysis combined with a QuEChERS method may be applicable to simultaneous determination of conazoles in garlic samples.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.16-25
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• 2019

The Internet of things (IOT) is remodeling the agribusiness empowering the agriculturists through the extensive range of strategies, for example, accuracy as well as practical farming to deal with challenges in the field. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. The objective of this research paper to present tools and best practices for understanding the role of information and communication technologies in agriculture sector, motivate and make the illiterate farmers to understand the best insights given by the big data analytics using machine learning. The methodology used in this system can monitor the humidity, moisture level and can even detect motions. According to the data received from all the sensors the water pump, cutter and sprayer get automatically activated or deactivated. we investigate a remote monitoring system using Wi-Fi. These nodes send data wirelessly to a central server, which collects the data, stores it and will allow it to be analyzed then displayed as needed and can also be sent to the client mobile.