• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2019년도 하계 학술발표초록집
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• pp.275-277
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• 2019

• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.66-66
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• 2023

• 한국작물학회:학술대회논문집
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• 한국작물학회 2020년도 추계학술대회
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• pp.67-67
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• 2020

• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.72-72
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• 2023

• 한국작물학회:학술대회논문집
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• 한국작물학회 2023년도 춘계학술대회
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• pp.45-45
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• 2023

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 춘계학술대회
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• pp.40-40
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• 2022

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 추계학술대회
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• pp.133-133
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• 2021

• 한국작물학회:학술대회논문집
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• 한국작물학회 2021년도 추계학술대회
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• pp.134-134
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• 2021

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.107-107
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• 2022

The increase in atmospheric temperature due to climate change prolongs the period of exposure to high-temperature environments during rice cultivation. In particular, high-temperature during early seed development greatly affects on the productivity and quality of rice. The high temperature at this time not only affects the transport and distribution of assimilates from leaves to seeds and the accumulation of starch in the seeds, but also affects the leaves, which are the production organs of assimilates, and increases the consumption of assimilation products due to an increase in respiration. Therefore, in this study, rice was grown in temperature gradient chambers(TGC) to analyze the effects of high temperature on physiological responses, assimilate production, and changes in gene expression in rice leaves. Analysis of chlorophyll and sugar contents and RNA-seq experiments were performed using flag leaves collected under normal and elevated temperature conditions, respectively, during the early seed development stage, and then these results were comprehensively discussed.

• Journal of information and communication convergence engineering
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• 제17권1호
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• pp.8-13
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• 2019

Smart Farming has been regarded as an important application in information and communications technology (ICT) fields. Selecting crops for cultivation at the pre-production stage is critical for agricultural producers' final profits because over-production and under-production may result in uncountable losses, and it is necessary to predict crop production to prevent these losses. The ITU-T Recommendation for Smart Farming (Y.4450/Y.2238) defines plan/production consultation service at the pre-production stage; this type of service must trace crop production in a predictive way. Several research papers present that machine learning technology can be applied to predict crop production after related data are learned, but these technologies have little to do with standardized ICT services. This paper clarifies the relationship between agricultural consultation services and predicting crop production. A prediction scheme is proposed, and the results confirm the usability and superiority of machine learning for predicting crop production.