Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data

Na, Sang-Il; Lee, Kyoungdo; Baek, Shin-Chul; Hong, Su Young

doi:10.7745/kjssf.2015.48.5.556

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Studies on food crops yield prediction, such as rice [19] and grain maize [20] in the southern area of the Korean Peninsula, have been actively performed. Similar studies were also carried out in economic plants, such as Chinese cabbage [21] and apple [22], in this region. For forage crops cultivated in this region, yield modeling studies based on climatic data were carried out on whole crop rye [23], Italian ryegrass [24], and whole crop maize [25].…”

Section: Introductionsupporting

confidence: 60%

Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea

2020

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The objective of this study was to construct a sorghum–sudangrass hybrid (SSH) yield prediction model based on climatic, soil, and cultivar information in the southern area of the Korean Peninsula. Besides, the effects of climatic factors on SSH yield were investigated simultaneously. The SSH dataset (n = 105), including Dry Matter Yield (DMY, kg/ha), Seeding-Harvest Accumulated Temperature (SHaAT, °C), Seeding–Harvest Accumulated Precipitation (SHAP, mm), Seeding–Harvest Sunshine Duration (SHSD, h), Soil Suitability Score (SSS), and cultivar maturity information, was developed for model construction. Subsequently, using general linear modeling method, the SSH yield prediction model was constructed as follows: DMY = 6.5SHaAT – 4.9SHAP + 13.8SHSD – 54.4SSS – 1036.4 + Maturity. The impacts of the accumulated thermal climatic variables and accumulated precipitation during crop growth on the variance of SSH yield in this region were confirmed. The summer-concentrated precipitation in the southern area of the Korean Peninsula exceeded the proper range of SSH water requirement and led to stresses to its yield production. Furthermore, to improve the data quality for high fitness model construction, the standard schedule for forage crop cultivation experiment in this region was recommended to be developed, especially under the data requirement in the context of the big data era.

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Section: Introductionsupporting

confidence: 60%

Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea

2020

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“…Therefore, the Korean government developed technology to solve the monitoring problem and to prepare supply and demand stability measures by predicting the production volume through the periodic monitoring of the highland cabbage growing area [11][12][13]. In the past, studies on agricultural land environment observation, crop production, and cultivation-area monitoring have been conducted using satellite imagery, mainly for agriculture [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Developing a p-NDVI Map for Highland Kimchi Cabbage Using Spectral Information from UAVs and a Field Spectral Radiometer

2020

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Kimchi cabbage grows in South Korea and is an essential ingredient for making kimchi with the kimjang method. The technique of accurately managing and monitoring crops such as kimchi cabbage plays an important role in stabilizing consumer prices. Unmanned aerial vehicles (UAVs) are expected to be used more widely in global and local agriculture. The agricultural sites at which kimchi cabbages are cultivated are affected by various climatic, terrain, and soil conditions, requiring technologies that can accurately and quickly acquire such information. UAVs and sensors are able to provide some of these data. In this study, we set up a cultivation environment for kimchi cabbage and investigated the correlation between a UAV-attached multispectral sensor and a field-portable spectroradiometer. Reflectance measurement using a spectroradiometer was performed on 99 kimchi cabbages in a Mt. Maebong testbed. We aimed to find a method for obtaining accurate vegetation information by combining the high spatial and temporal resolution information of the UAV observation with the spectral resolution of the spectroradiometer. Spectral analysis was used to identify the difference between healthy and poorly growing cabbage and to find the wavelength that most affected the growth. The hyperspectrum of the spectroradiometer reflected the accurate vegetation characteristics and contributed greatly to the identification of vegetation indices. A method for correcting the errors that occurred in the ground and UAV monitoring and the difference arising from the application of the broadband wavelength of the UAV and the single wavelength of the spectroradiometer through correlation analysis is presented. The calibration equation method was applied to UAV spatial information and was used to create a precise normalized distribution vegetation index (p-NDVI) map. The p-NDVI map was organized into four categories for the selection of cabbages with healthy (good) growth. Our results show that (1) the merged spectral analysis method was found to be more accurate and distinct than conventional methods, and (2) methods for estimating cabbage growth status showed a higher significant correlation than the UAV-based NDVI. At the maturity stage, high accuracy (R2 = 0.7816, RMSE = 0.06) was achieved for NDVI. Although this map is the result of the limited vegetation monitoring of UAV images taken during the maturity stage, it could be of great help for managing the quality and production of cabbage. However, the efficient management of highland kimchi cabbage requires continuous research under various conditions to enable periodic and systematic monitoring using UAVs and sensors.

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Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

Na¹,

Hong²,

Park³

et al. 2016

Korean Journal of Soil Science and Fertilizer

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For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, NDVI UAV and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVI UAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVI UAV and other agro-meteorological factors were well reflected in the model.

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Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data

Cited by 4 publications

References 9 publications

Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea

Yield Prediction Modeling for Sorghum–Sudangrass Hybrid Based on Climatic, Soil, and Cultivar Data in the Republic of Korea

Developing a p-NDVI Map for Highland Kimchi Cabbage Using Spectral Information from UAVs and a Field Spectral Radiometer

Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

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