• Journal of Biosystems Engineering
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• v.29 no.1
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• pp.65-70
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• 2004

This study was carried out to verify and predict the soil informations such as the contents of organic matter(OM) and Mg and pH of the soil. The predictability of spacial variation in the paddy field was examined by analyzing the various soil information. The prediction models for the OM pH, and Mg, were developed using inverse distance weighted (IDW), triangulated irregular network(TIN) and Kriging model. The determination of coefficients of linear and spherical Kriging models were 0.756 and 0.578, respectively, and were very low in comparison with other soil information. For IDW and TIN model, the determination of coefficients were 1.000 and hence the performance of the models was found to be excellent. The developed models were validated using unknown soil sample obtained In 2000 and 2001. From the analysis of relationship between the measured pH and predicted 0.9353. For prediction of Mg, the determination of coefficient is more than 0.8. Since the determination of coefficients of developed models for OM were relatively low, it may be difficult to predict the content of OM using the developed models. For further study, the additional works to enhance the performance of the prediction models for soil information are required.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.150-150
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• 2019

The development of prolong and severe drought can directly impact on the environment, agriculture, economics and society of country. A lot of efforts have been made across worldwide in the planning, monitoring and mitigation of drought. Currently, different drought indices such as the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) are developed and most commonly used to monitor drought characteristics quantitatively. However, it will be very meaningful and essential to develop a more effective technique for assessment and monitoring of onset and end of drought. Therefore, in this study, the hidden Markov Bayesian classifier (MBC) was employed for the assessment of onset and end of meteorological drought classes. The results showed that the probabilities of different classes based on the MBC were quite suitable and can be employed to estimate onset and end of each class for meteorological droughts. The classification results of MBC were compared with SPI and with past studies which proved that the MBC was able to account accuracy in determining the accurate drought classes. For more performance evaluation of classification results confusion matrix was used to find accuracy and precision in predicting the classes and their results are also appropriate. The overall results indicate that the MBC was effective in predicating the onset and end of drought events and can utilized for monitoring and management of short-term drought risk.

• Journal of Climate Change Research
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• v.8 no.3
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• pp.247-255
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• 2017

In this study, GHG inventory on 17 local government between 2005 and 2014 is build up using 'GHG emission estimation guideline (2016. 2) for local government' developed and distributed by KECO. This covers all the sectors should be included in national GHG inventory, which are energy, industrial process, agriculture, AFOLU, and waste. In addition, six GHGs, carbon dioxide, metane, nitrous oxide, hydrofluorocarbons, perfluorocarbons, sulphur hexafluoride declared in Kyoto protocol are estimated to reflect utmost precision. Indirect esissions, such as electricity, heat and waste generation are separately estimated as well as direct emissions to help local government to establish substantial and implementable reduction measures of GHGs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.217-220
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• 2014

Wireless sensor network（WSN） consists by a large number of low-cost micro-sensor nodes, collaborate to achieve the perception of information collection, processing and transmission tasks in deployment area. It can be widely used in national defense, intelligent transportation, medical care, environmental monitoring, precision agriculture, and industrial automation and many other areas. One of the key technologies of sensor networks is the data maintenance management technology. In this paper we analyze the data management technology of wireless sensor network and pointed their problems.

• Smart Media Journal
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• v.11 no.4
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• pp.38-45
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• 2022

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.57-66
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• 2023

Rice is an important food crop for most of the population in the world and it is largely cultivated in Pakistan. It not only fulfills food demand in the country but also contributes to the wealth of Pakistan. But its production can be affected by climate change. The irregularities in the climate can cause several diseases such as brown spots, bacterial blight, tungro and leaf blasts, etc. Detection of these diseases is necessary for suitable treatment. These diseases can be effectively detected using deep learning such as Convolution Neural networks. Due to the small dataset, transfer learning models such as vgg16 model can effectively detect the diseases. In this paper, vgg16, inception and xception models are used. Vgg16, inception and xception models have achieved 99.22%, 88.48% and 93.92% validation accuracies when the epoch value is set to 10. Evaluation of models has also been done using accuracy, recall, precision, and confusion matrix.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3003-3029
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• 2023

The rapid evolution of the IoT has paved the way for new opportunities in smart city domains, including e-health, smart homes, and precision agriculture. However, this proliferation of services demands effective SLAs between customers and service providers, especially for critical services. Difficulties arise in maintaining the integrity of such agreements, especially in vulnerable wireless environments. This study proposes a novel SLA management model that uses an SDN-Enabled WSN consisting of wireless nodes to interact with smart contracts in a straightforward manner. The proposed model ensures the persistence of network metrics and SLA provisions through smart contracts, eliminating the need for intermediaries to audit payment and compensation procedures. The reliability and verifiability of the data prevents doubts from the contracting parties. To meet the high-performance requirements of the blockchain in the proposed model, low-cost algorithms have been developed for implementing blockchain technology in wireless sensor networks with low-energy and low-capacity nodes. Furthermore, a cryptographic signature control code is generated by wireless nodes using the in-memory private key and the dynamic random key from the smart contract at runtime to prevent tampering with data transmitted over the network. This control code enables the verification of end-to-end data signatures. The efficient generation of dynamic keys at runtime is ensured by the flexible and high-performance infrastructure of the SDN architecture.

• Economic and Environmental Geology
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• v.57 no.3
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• pp.329-342
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• 2024

The exponential increase in nitrate pollution of river water poses an immediate threat to public health and the environment. This contamination is primarily due to various human activities, which include the overuse of nitrogenous fertilizers in agriculture and the discharge of nitrate-rich industrial effluents into rivers. As a result, the accurate prediction and identification of contaminated areas has become a crucial and challenging task for researchers. To solve these problems, this work leads to the prediction of nitrate contamination using machine learning approaches. This paper presents a novel approach known as Grey Wolf Optimizer (GWO) based on the Stacked Ensemble approach for predicting nitrate pollution in the Cauvery Delta region of Tamilnadu, India. The proposed method is evaluated using a Cauvery River dataset from the Tamilnadu Pollution Control Board. The proposed method shows excellent performance, achieving an accuracy of 93.31%, a precision of 93%, a sensitivity of 97.53%, a specificity of 94.28%, an F1-score of 95.23%, and an ROC score of 95%. These impressive results underline the demonstration of the proposed method in accurately predicting nitrate pollution in river water and ultimately help to make informed decisions to tackle these critical environmental problems.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Korean Journal of Environmental Agriculture
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• v.36 no.3
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• pp.154-160
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• 2017

BACKGROUND:This study was carried out to establish an efficient sample preparation for the simultaneous determination of bisphenols (BPs) in river water samples using gas chromatography-mass spectrometry (GC-MS). Sample preparation was examined with conventional extraction methods, such as solid-phase extraction (SPE) and liquid-liquid extraction (LLE), and their efficiency was compared with validation results, including linearity of calibration curve, method detection limit (MDL), limit of quantification (LOQ), accuracy, and precision. METHODS AND RESULTS:The BPs (bisphenol A, BPA; bisphenol B, BPB; bisphenol C, BPC; bisphenol E, BPE; bisphenol F, BPF; bisphenol S, BPS) were analyzed using GC-MS. The range of MDLs by SPE and LLE methods was $0.0005{\sim}0.0234{\mu}g/L$ and $0.0037{\sim}0.2034{\mu}g/L$, and that of LOQs was $0.0015{\sim}0.0744{\mu}g/L$ and $0.0117{\sim}0.6477{\mu}g/L$, respectively. The calibration curve obtained from standard solution of $0.004{\sim}4.0{\mu}g/L$ (SPE) and $0.016{\sim}16{\mu}g/L$ (LLE) showed good linearity with $r^2$ value of 0.9969 over. Accuracy was 93.2~108% and 97.4~120%, and precision was 1.7~4.6% and 0.7~6.5%, respectively. The values of MDL and LOQ resulted from the SPE method were higher than those from the LLE method, particularly those values of BPA were highest among the BPs. Based on the results, the SPE method was applied to determine the BPs in river water samples. Water samples were collected from mainstream, tributary and sewage wastewater treatment plants (SWTPs) in the Yeongsan river basin. The concentration of BPB, BPC, BPE, BPF and BPS were not detected in all sites, whereas BPA was ranged $0.0095{\sim}0.2583{\mu}g/L$, which was $0.0166{\sim}0.0810{\mu}g/L$ for mainstreams, $0.0095{\sim}0.2583{\mu}g/L$ for tributaries, $0.0352{\sim}0.1217{\mu}g/L$ for SWTPs. CONCLUSION: From these results, the SPE method was very effective for the simultaneous determination of BPs in river water samples using GC-MS. We provided that it is a convenient, reliable and sensitive method enough to monitor and understand the fate of the BPs in aquatic ecosystems.