• Proceedings of the Korean Society of Crop Science Conference
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• 2004.04a
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• pp.100-101
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• 2004

[ $\bigcirc$ ] In the growth simulation using genetic coefficients calculated with fooled data under various condition, WAGT was not higher and LAI, WLVG, WSO were higher, but WST was similar before grain-filling stage after the became lower because of higher translocation of carbohydrates than in the growth simulation using genetic coefficients calculated with data under high nitrogen applicated condition. $\bigcirc$ Genetic coefficients should be calculated with data showing potential in ORYZA2000, but under 180 kg and 240 kg N condition in 2003, plants were infected by panicle blast and also yield was not higher than under 120 kg N condition showing not potential condition and therefore not appropriate for genetic coefficients estimation compared with pooled data from various condition.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.4
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.

• 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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• 2017.06a
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• pp.46-46
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• 2017

We developed a rice simulation model for remote-sensing (SIMRIW-RS, Homma et al., 2007) to evaluate rice production and management on a regional scale. Here, we reports its application trial to estimate rice production in farmers' fields in Sendai, Japan. The remote-sensing data for the application was periodically obtained by multispectral camera (RGB + NIR and RedEdge) attached with unmanned air vehicle (UAV). The airborne images was 8 cm in resolution which was attained by the flight at an altitude of 115 m. The remote-sensing data was relatively corresponded with leaf area index (LAI) of rice and its spatial and temporal variation, although the correspondences had some errors due to locational inaccuracy. Calibration of the simulation model depended on the first two remote-sensing data (obtained around one month after transplanting and panicle initiation) well predicted rice growth evaluated by the third remote-sensing data. The parameters obtained through the calibration may reflect soil fertility, and will be utilized for nutritional management. Although estimation accuracy has still needed to be improved, the rice yield was also well estimated. These results recommended further data accumulation and more accurate locational identification to improve the estimation accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.68-78
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• 2020

Crop models have been used to predict vegetable crop yield, which would have a considerable economic impact on consumers as well as producers. A small number of models have been developed to estimate growth and yield of vegetables due to limited availability of growth observation data in high-quality. In this study, we aimed to analyze the protocols designed for collection of the observation data for major vegetable crops including cabbage, radish, garlic, onion and pepper. We also designed the protocols suitable for development and verification of a vegetable crop growth model. In particular, different measures were proposed to improve the existing protocol used by Statistics Korea (KOSTAT) and Rural Development Administration (RDA), which would enhance reliability of parameter estimation for the crop model. It would be advantageous to select sampling sites in areas where reliable weather observation data can be obtained because crop models quantify the response of crop growth to given weather conditions. It is recommended to choose multiple sampling sites where climate conditions would differ. It is crucial to collect time series data for comparison between observed and simulated crop growth and yield. A crop model can be developed to predict actual yield rather than attainable yield using data for crop damage caused by diseases and pests as well as weather anomalies. A bigdata platform where the observation data are to be shared would facilitate the development of crop models for vegetable crops.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.143-143
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• 2017

Wheat is one of the most important crops in production and consumption. Despite increasing of importance, the self-sufficiency of wheat is less than 2% in Korea. To improve yield potential and broaden the genetic pool of common wheat in Korea, introduction of alien germplasms into the Korean wheat breeding program is suggested. For effective utilization of the germplasm, we introduced total 1,195 germplasms from the world, which were provided by National Plant Germplasm System (NPGS, USDA) and evaluated the yield, field performances and agronomic traits for 8 years. Among 55 countries, germplasms from Canada, Ethiopia, Mexico and United States accounts for 78%, especially germplasms collected from United States accounts for 50%. Yield comparison of germplasms and collected region analysis indicate that the high yielding germplasms are collected from countries or states of particular range of latitude. The combination analysis of the yield and agronomic traits and the geographical information of collected region will be utilized for improving Korean wheat breeding programs.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.408-417
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• 2016

Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% ($R^2=0.99$). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.81-81
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• 2022

Recently, many studies on big data based smart farming have been conducted. Research to quantify morphological characteristics using image data from various crops in smart farming is underway. Rice is one of the most important food crops in the world. Much research has been done to predict and model rice crop yield production. The number of productive tillers per plant is one of the important agronomic traits associated with the grain yield of rice crop. However, modeling the basic growth characteristics of rice requires accurate data measurements. The existing method of measurement by humans is not only labor intensive but also prone to human error. Therefore, conversion to digital data is necessary to obtain accurate and phenotyping quickly. In this study, we present an image-based method to predict leaf number and evaluate tiller number of individual rice crop using YOLOv5 deep learning network. We performed using various network of the YOLOv5 model and compared them to determine higher prediction accuracy. We ako performed data augmentation, a method we use to complement small datasets. Based on the number of leaves and tiller actually measured in rice crop, the number of leaves predicted by the model from the image data and the existing regression equation were used to evaluate the number of tillers using the image data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.7
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• pp.599-607
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• 2006

The experiments were carried out to develop simulation model for estimating the yield of soybean in upland and paddy field condition. Field experiments were done at National Institute of Crop Science in 2005. The evaluated soybean cultivars were Taekwangkong, Daewonkong, and Hwangkeumkong. Soybean seeds were planted by hill seeding with 3-4 seeds and row and hill spacing were $60{\times}10cm$ in upland and $60{\times}15cm$ in paddy field. Seeds were sown on row (without making ridge) and on the top of ridge in upland and paddy field, respectively. Field parameters were measured yield components ($plants/m^{2}$, pod no./plant, and 100-seed weight, seed yield and growth characteristics (stem length, leaf area at each stage, and dry weight of shoot) and after measuring they were compared the relationships with seed yield and yield components and seed yield and growth characteristics. Seed yield of soybean was affected by cultivars and planting density. Seed yield was higher in upland than paddy field due to the higher planting density in upland field. The upland soybeans generally had lower 100-seed weight than that of paddy field. Seed yield of soybean in a paddy field was greatest in Taekwangkong and followed by Daewonkong and Hwangkeumkong. The harvest index of taekwangkong and Hwanggumkong was higher in upland than paddy field, however, it was higher in paddy field than upland in Daewonkong. Seed yield was greatest in Daewonkong in both experimental fields. The greatest stem length was observed in taekwangkong and Hwanggumkong (R6) in late growth stage in paddy field. Dry weight of shoot and pod, pod number, stem length, and stem diameter were higher grown in paddy field than grown in upland. Crop growth rate (CGR) of cultivars was higher in paddy field after 8 WAS(weeks after sowing) and it was greatest at 13 WAS in Daewonkong among the cultivars. In upland field, CGR was greatest in Taekwangkong and then followed by Daewonkong and Hwanggumkong during 12 and 15 WAS. There was no significant relationships between 100-seed weight and seed yield in both experimental fields. A significant positive relationship was observed between seed number and seed yield. The correlation coefficients between leaf area and shoot dry weight were about 0.8 during the whole growth stage except 5 WAS and 4-5 WAS in paddy field and upland, respectively. This experiment was done just one year and drained paddy field condition was not satisfied drained condition successfully at 7th leaf age of soybean by the heavy rain, so we suggest that the excessive soil water reduced seed yield in paddy field and the weather condition should be considered for utilizing of these results.

• Korean Journal of Medicinal Crop Science
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• v.25 no.2
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• pp.115-120
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• 2017

Background: The study aimed to obtain data on the effects of cultivation and soil reduction of green manure crop on the quantity and quality of organically cultivated Cynanchum wilfordii Hemsley. Methods and Results: The experiment comprised four treatments: control, hairy vetch, barley, and hairy vetch + barley (3 : 2). The plant height in the hairy vetch treatment (86.3 cm) was significantly different from that in the other treatments, whereas the stem diameter leaf area, and special product analysis division (SPAD) value did not differ across the treatments. The largest soil reduction of green manure crop was recorded in the barley treatment (440 kg/10 a), whereas the smallest was recorded in the single treatment with hairy vetch (80 kg/10 a). The hairy vetch + barley (60 : 40) treatment showed 63% more soil microorganisms than control. Radical scavenging activity estimation revealed that the total polyphenol content was highest (1,740 mg/kg), and the 2,2-diphenyl-1-picrylhydrazyl (DPPH) was 92.6% in the barley treatment. The 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) activation was highest in the control (51.1%), and the root yield was the highest in the barley treatment (310 kg/10 a). Conclusions: The root yield, total polyphenol content, and antioxidant activity of Cynanchum wilfordii (Maxim.) Hemsley increased in presence of the green manure crop barley.