• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.45-45
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• 2017

On-site monitoring of vegetable growth parameters, such as leaf length, leaf area, and fresh weight, in an agricultural field can provide useful information for farmers to establish farm management strategies suitable for optimum production of vegetables. Unmanned Aerial Vehicles (UAVs) are currently gaining a growing interest for agricultural applications. This study reports on validation testing of previously developed vegetable growth estimation models based on UAV-based RGB images for white radish and Chinese cabbage. Specific objective was to investigate the potential of the UAV-based RGB camera system for effectively quantifying temporal and spatial variability in the growth status of white radish and Chinese cabbage in a field. RGB images were acquired based on an automated flight mission with a multi-rotor UAV equipped with a low-cost RGB camera while automatically tracking on a predefined path. The acquired images were initially geo-located based on the log data of flight information saved into the UAV, and then mosaicked using a commerical image processing software. Otsu threshold-based crop coverage and DSM-based crop height were used as two predictor variables of the previously developed multiple linear regression models to estimate growth parameters of vegetables. The predictive capabilities of the UAV sensing system for estimating the growth parameters of the two vegetables were evaluated quantitatively by comparing to ground truth data. There were highly linear relationships between the actual and estimated leaf lengths, widths, and fresh weights, showing coefficients of determination up to 0.7. However, there were differences in slope between the ground truth and estimated values lower than 0.5, thereby requiring the use of a site-specific normalization method.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.57-74
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• 2005

Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, the two-year experiments were conducted in 2002 and 2003 in a large-scale rice field of $6,600m^2$ In year 2004, an experiment was conducted to know if prescribed N for site-specific fertilizer management at panicle initiation stage (VRT) could reduce spatial variation in yield and protein content of rice while increasing yield compared to conventional uniform N topdressing (UN, ,33 kg N/ha at PIS) method. The trial field was subdivided into two parts and each part was subjected to UN and VRT treatment. Each part was schematically divided in $10\times10m$ grids for growth and yield measurement or VRT treatment. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of $6.8\%$, natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations to predict rice yield and protein content from plant growth parameters at panicle initiation stage (PIS) and N uptake from PIS to harvest. This model equations were developed from the data obtained from the previous two-year experiments. The plant growth parameters for this calculation were predicted non-destructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be $60\%$ according to the previous reports. The prescribed VRT N ranged from 0 to 110kg N/ha with average of 57kg/ha that was higher than 33kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960kg/ha. The coefficient of variation (CV) for rice yield and protein content was reduced significantly to $8.1\%\;and\;7.1\%$ in VRT from $14.6\%\;and\;13.0\%$ in UN, respectively. And also the average protein content of milled rice in VRT showed very similar value of target protein content of $6.8\%$. Although N use efficiency of VRT compared to UN was not quantified due to lack of no N control treatment, the procedure used in this paper for VRT estimation was believed to be reliable and promising method for managing within-field spatial variability of yield and protein content. The method should be received further study before it could be practically used for site-specific crop management in large-scale rice field.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.167-174
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• 2019

A process-oriented crop growth model can simulate the biophysical process of rice under diverse environmental and management conditions, which would make it more versatile than an empirical crop model. In the present study, we examined chronology and background of the development of the rice growth models in Korea, which would provide insights on the needs for improvement of the models. The rice crop growth models were introduced in Korea in the late 80s. Until 2000s, these crop models have been used to simulate the yield in a specific area in Korea. Since then, improvement of crop growth models has been made to take into account biological characteristics of rice growth and development in more detail. Still, the use of the crop growth models has been limited to the assessment of climate change impact on crop production. Efforts have been made to apply the crop growth model, e.g., the CERES-Rice model, to develop decision support system for crop management at a farm level. However, the decision support system based on a crop growth model was attractive to a small number of stakeholders most likely due to scarcity of on-site weather data and reliable parameter sets for cultivars grown in Korea. The wide use of the crop growth models would be facilitated by approaches to extend spatial availability of reliable weather data, which could be either measured on-site or estimates using spatial interpolation. New approaches for calibration of cultivar parameters for new cultivars would also help lower hurdles to crop growth models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.4
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• pp.247-255
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• 2005

ice yield and plant growth response to nitrogen (N) fertilizer may vary within a field, probably due to spatially variable soil conditions. An experiment designed for studying the response of rice yield to different rates of N in combination with variable soil conditions was carried out at a field where spatial variation in soil properties, plant growth, and yield across the field was documented from our previous studies for two years. The field with area of 6,600 m2 was divided into six strips running east-west so that variable soil conditions could be included in each strip. Each strip was subjected to different N application level (six levels from 0 to 165kg/ha), and schematically divided into 12 grids $(10m \times10m\;for\;each\;grid)$ for sampling and measurement of plant growth and rice grain yield. Most of plant growth parameters and rice yield showed high variations even at the same N fertilizer level due to the spatially variable soil condition. However, the maximum plant growth and yield response to N fertilizer rate that was analyzed using boundary line analysis followed the Mitcherlich equation (negative exponential function), approaching a maximum value with increasing N fertilizer rate. Assuming the obtainable maximum rice yield is constrained by a limiting soil property, the following model to predict rice grain yield was obtained: $Y=10765{1-0.4704^*EXP(-0.0117^*FN)}^*MIN(I-{clay},\;I_{om},\;I_{cec},\;I_{TN},\; I_{Si})$ where FN is N fertilizer rate (kg/ha), I is index for subscripted soil properties, and MIN is an operator for selecting the minimum value. The observed and predicted yield was well fitted to 1:1 line (Y=X) with determination coefficient of 0.564. As this result was obtained in a very limited condition and did not explain the yield variability so high, this result may not be applied to practical N management. However, this approach has potential for quantifying the grain yield response to N fertilizer rate under variable soil conditions and formulating the site-specific N prescription for the management of spatial yield variability in a field if sufficient data set is acquired for boundary line analysis.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.2
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• pp.92-103
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• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.

• Agricultural and Biosystems Engineering
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• v.6 no.2
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• pp.39-46
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• 2005

Because soil compaction is a concern in crop production and environmental pollution, quantification and management of spatial and vertical variability in soil compaction for soil strength) would be a useful aspect of site -specific field management. In this paper, a soil strength profile sensor (SSPS) that could take measurements continuously while traveling across the field was developed and the performance was evaluated through laboratory and field tests. The SSPS obtained data simultaneously at 5 evenly spaced depths up to 50 em using an array of load cells, each of which was interfaced with a soil-cutting tip. Means of soil strength measurements collected in adjacent, parallel transects were not significantly different, confirming the repeatability of soil strength sensing with the SSPS. Maps created with sensor data showed spatial and vertical variability in soil strength. Depth to the restrictive layer was different for different field locations, and only 5 to 16% of the tested field areas were highly compacted.

• Korean Journal of Medicinal Crop Science
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• v.22 no.6
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• pp.429-434
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• 2014

This study describes the efficient method for the discrimination of 'Cheonryang' in Panax ginseng Meyer using a STS primer. A total of 208 STS primers were applied to polymerase chain reaction (PCR) amplification for discriminating Korean ginseng cultivars. Co-dominant polymorphic band patterns were generated with two primers, MFGp 0019, MFGp 0248, and successful identification of 'Cheonryang' was achieved from out of 11 Korean ginseng cultivars. Two different sizes of DNA band patterns were detected with MFGp 0019 primer. Ten Korean ginseng cultivars shared the same size of amplified DNAs (389 bp), but 'Cheonryang' showed a different size. Thus 'Cheonryang' can be efficiently distinguished from the other ten ginseng cultivars by using the MFGp 0019 primer. In the case of MFGp 0248, two different sizes of DNA band patterns were detected in the eleven ginseng cultivars. Same sized amplified DNA bands (307 bp) were shown in five cultivars (Chunpoong, Gopoong, Kumpoong, Cheongsun, Sunhyang) and 254 bp sized DNA bands were identified in the other 6 cultivars (Yunpoong, Sunpoong, Sunun, Sunone, Cheonryang, K-1). In conclusion, the two STS primers, MFGp 0019, and MFGp 0248, provide a rapid and reliable method for the specific identification of 'Cheonryang' cultivar from a large number of samples.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.196-202
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• 2020

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91~0.92, GNDVI were 0.92~0.94, NGRDI were 0.71~0.85 and NDREI were 0.84~0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2023.04a
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• pp.25-25
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• 2023

As the demand for processing potatoes increases, imports of raw potatoes and potato products are increasing, so it is necessary to expand potato production as raw materials for processing in Korea. Potato varieties for processing that can be grown in fall have been developed, but research on cultivation technology and processing quality management technology to improve chip processing quality is very insufficient. Therefore, this study was conducted to investigate the optimal harvest time by investigating the quantity and chipping characteristics of potato chips during autumn cultivation. As the test varieties, the chip processing varieties "Saebong", "Eunsun", and "Geumnaru" were used, and the potato cultivation site was the Seocheon-gun Test field (214 Gaeya-ri) of the Chungcheongnam-do. The test treatment was at harvest time after spring cultivation, and the potatoes were harvested at 70, 80, 90, and 100 days after sowing based on the sowing time. The investigation items were potato productivity (total yield, yield of standard processing, and number of tubers) and chip-processing characteristics (chip color, dry matter content, glucose content, etc.). As a result of examining the yield characteristics according to the harvest time, statistical significance was not found according to the treatment. The total yield (ton/ha) was 27.5 to 30.5, and there was no significant difference depending on the time of 70 to 100 days after harvest. The standard quantity for processing (yield of 81-250g potatoes per unit) also showed a similar trend. In chipping characteristics according to harvest time, statistical significance was high in specific gravity and glucose content. The specific gravity was highest at 1.077 at 70 days after harvest, and the glucose (mg/dL) content was the lowest at 37.5 at 80 days after harvest. Statistical significance was not recognized, but chip color (L value) was the highest at 64.4 at 70 days after harvest. Therefore, it is judged that the optimal harvesting time for chip processing is 70 to 80 days after sowing.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.159-164
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• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.