• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.169-169
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• 2022

Over the past 100 years, the global average temperature has risen by 0.75 ℃. The Korean Peninsula has risen by 1.8 ℃, more than twice the global average. According to the RCP 8.5 scenario, the CO₂ concentration in 2100 will be 940 ppm, about twice as high as current. The National Institute of Crop Science(NICS) is using the SPAR (Soil-Plant Atmosphere Research) facility that can precisely control the environment, such as temperature, humidity, and CO₂. A Python-based colony photosynthesis algorithm has been developed, and the carbon and nitrogen absorption rate of rice is evaluated by setting climate change conditions. In this experiment, Oryza Sativa cv. Shindongjin were planted at the SPAR facility on June 10 and cultivated according to the standard cultivation method. The temperature and CO₂ settings are high temperature and high CO₂ (current temperature+4.7℃ temperature+4.7℃·CO₂ 800ppm), high temperature single condition (current temperature+4.7℃·CO₂ 400ppm) according to the RCP8.5 scenario, Current climate is set as (current temperature·CO₂400ppm). For colony photosynthesis measurement, a LI-820 CO₂ sensor was installed in each chamber for setting the CO₂ concentration and for measuring photosynthesis, respectively. The colony photosynthetic rate in the booting stage was greatest in a high temperature and CO₂ environment, and the higher the nitrogen fertilization level, the higher the colony photosynthetic rate tends to be. The amount of photosynthesis tended to decrease under high temperature. In the high temperature and high CO₂ environment, seed yields, the number of an ear, and 1000 seed weights tended to decrease compared to the current climate. The number of an ear also decreased under the high temperature. But yield tended to increase a little bit under the high temperature and high CO₂ condition than under the high temperature. In addition, In addition to this study, it seems necessary to comprehensively consider the relationship between colony photosynthetic ability, metabolite reaction, and rice yield according to climate change.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.180-181
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• 2005

• Journal of Biosystems Engineering
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• v.39 no.1
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• pp.47-56
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• 2014

Purpose: In protected crop production facilities such as greenhouse and plant factory, farmers should be present and/or visit frequently to the production site for maintaining optimum environmental conditions and better production, which is time and labor consuming. Monitoring of environmental condition is highly important for optimum control of the conditions, and the condition is not uniform within the facility. Objectives of the paper were to investigate spatial and vertical variability in ambient environmental variables and to provide useful information for sensing and control of the environments. Methods: Experiments were conducted in a strawberry-growing greenhouse (greenhouse 1) and a cherry tomato-growing greenhouse (greenhouse 2). Selected ambient environmental variables for experiment in greenhouse 1 were air temperature and humidity, and in greenhouse 2, they were air temperature, humidity, PPFD (Photosynthetic Photon Flux Density), and $CO_2$ concentration. Results: Considerable spatial, vertical, and temporal variability of the ambient environments were observed. In greenhouse 1, overall temperature increased from 12:00 to 14:00 and increased after that, while RH increased continuously during the experiments. Differences between the maximum and minimum temperature and RH values were greater when one of the side windows were open than those when both of the windows were closed. The location and height of the maximum and minimum measurements were also different. In greenhouse 2, differences between the maximum and minimum air temperatures at noon and sunset were greater when both windows were open. The maximum PPFD were observed at a 3-m height, close to the lighting source, and $CO_2$ concentration in the crop growing regions. Conclusions: In this study, spatial, vertical, and temporal variability of ambient crop growing conditions in greenhouses was evaluated. And also the variability was affected by operation conditions such as window opening and heating. Results of the study would provide information for optimum monitoring and control of ambient greenhouse environments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.4
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• pp.388-393
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• 2008

Earth observation satellite imagery having medium-resolution can provide the useful information very rapidly and cheaply. The objective of this study was to assess the feasibility for monitoring rice growth and yield using medium resolution satellite imagery at Seosan AB reclaimed area, Chung-nam province. Using the LANDSAT imagery at booting stage ($29^{th}$ July 2004), $NDVI_R$ had the most significant linear relationships with rice yield of Seosan AB reclaimed area with the correlation coefficient (r) as 0.68. Therefore, this relationship was established as rice yield equation as function of $NDVI_R$, where excluding the 10 small area having low number of pixel, the determination coefficient ($R^2$) of the linear regression between NDVIred and milled rice yield was improved to 0.66. In addition, raster masking method, which was easier and faster even if a little unaccurate than preexisting method, was established for extracting information paddy field zone. Adaptability of rice yield equation function of $NDVI_R$ on year and region was investigated using rice yield and $NDVI_R$ values, which were extracted with raster masking method, from 7 counties or cities, Kyeong-ki province in 2005. Relationship between observed and calculated rice yield showed 1:1 line indicating that the adaptability was admitted.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1891-1899
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• 2016

Recently food problem and crop disaster have been increased continuously because of the meteorological changes. These cause rising cost for crops continuously and irregularly. Some researchers have studied straight structure of device for hydroponics and plant factory previously to solve a fundamental part of these problems. However, there are several problems such as limited crop cultivation space, providing irregular nutrients for crops, and lack of monitoring interfaces. For them, we propose an optimal growth and development crops management system using light source tracking and recirculation of nutrient solution method to supply nutrient continuously based on IoT. In order to evaluate the performance of our system, we compared and analyzed in terms of two viewpoints, the tracking analysis for natural light source measurement and the growth of crops through artificial light, LED, respectively. We confirmed that the higher the duty ratio of LED, the larger the crop's size, particularly. As well as, for about 1 month, we compared with the existing natural light growing environment and that of our system. It was confirmed that the size of the crops grown through our system is about three times larger than that of natural light natural crops.

• Journal of The Korean Society of Agricultural Engineers
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• v.48 no.7
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• pp.65-72
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• 2006

Event mean concentration (EMC) of nitrogen (N) and phosphorus (P) is primary information for non-point source pollution assessment of a watershed. The EMCs for various types of agriculture such as dairy and crop farming under different climate and geologic conditions are not fully investigated. A diary- and cropfarming complex agricultural watershed in Piedmont region in Maryland, USA has been monitored for 10 years as a section 319 national monitoring program of US EPA. Dairy manure was the main source of fertilizer for crop farming in this watershed. Observed mean concentrations of N and P for each event were analyzed. Distribution of EMCs for N and P showed a wide range of variations. Representative EMCs of T-N and $NO_{3}-N$ tended to be higher than those reported for other agricultural watersheds. This study confirmed that site-specific EMC information for various agricultural practices is required for better assessment of non-point source pollution using EMC method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.153-153
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• 2022

There is a growing need to develop ion sensors for agriculture. As a result, several technologies have been developed, such as colorimetry, spectrophotometry, and ion-selective electrode (ISE). Among them, ISE has some advantages compared to others. First, it does not require pre-treatment processes and expensive equipment. Second, it is possible for the portable detection system by introducing small-sized electrodes. Finally, real-time and multiple detections of several ions are pursued. It is well-known that N, P, and K nutrients are critical for crop growth. With the development of agriculture techniques, the importance of soil nutrient analysis has attracted much attention for cost-effective and eco-friendly agriculture. Among several issues, minimizing the use of fertilizers is significant through quantitative analysis of soil nutrients. As a result, it is highly important to analyze certain nutrients, such as N (ammonium ion, nitrate ion, nitrite ion), P (dihydrogen phosphate ion, monohydrogen phosphate ion), and K (potassium ion). Therefore, developing sensors for accurate analysis of soil nutrients is highly desired. n this study, several ISEs have been fabricated to detect N, P, and K. Their performance has been intensively studied, such as sensitivity, selectivity coefficient, and concentration range, and compared with commercialized ISEs. In addition, preliminary tests on the in-situ N, P, and K monitoring have been conducted inside the soil.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.43-48
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• 2001

Many applications in the areas of agricultural, hydrological and environmental resource management require data over very large areas and with a high imaging frequency - monitoring crop growth, water stress, seasonal wetland flooding and natural vegetation development. This precludes the use of fine resolution data (Landsat, Spot) on the grounds of cost, accessibility and low imaging frequency. Meteorological satellites have the potential to fill this need, given their very wide spatial coverage, and high repeat imaging. The Remote Sensing Unit (RSU) at the Zambia Meteorological Department routinely receives, processes and archives imagery from both Meteosat and NOAA AVHRR satellites. Here I wish to present some examples of applications of these data sets that arise from the RSU work - relationships between rainfall and vegetation development as assessed by satellite, derived information and seasonal patterns of flooding in the Barotse floodplain and the Kafue flats. I also wish to outline ways in which a more widespread use of this data by the Zambian institutions canbe achieved.