• International Journal of Industrial Entomology and Biomaterials
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• v.8 no.1
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• pp.67-71
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• 2004

Ten improved mulberry varieties (Vl, C1730, C2016, C2017, Anantha, RFS-175, Thallaghatapura, Vishala, S1 and S1635) were evaluated through enzyme assay and estimation of soluble protein content followed by regression analysis, grown under irrigated conditions in the alluvial soils of Gangetic plains of West Bengal in India for five successive crops in a year, The nitrate reductase (EC No. 1.6.6.1) activity (NRA, $\mu$mol N $O_2$- $h^{-1}$ $g^{-1}$ fr, wt.), total soluble protein (mg $g^{-1}$ fr, wt.) was estimated which showed to vary significantly in the tested varieties. In addition to these, the other parameters like unit leaf fresh and dry weight (g), moisture ％, unit leaf area ($\textrm{cm}^2$), specific leaf weight (g c $m^{-2}$ ), total soluble sugar (mg $g^{-1}$ fr, wt.), leaf yield/plant (kg), shoot yield/plant (kg) and net photosynthetic rate (NPR, $\mu$㏖ $m^{2}$ $s^{-1}$ ) were also studied which showed to vary significantly in tested varieties. Among them, S1635, haying higher NRA (13.25 $\mu$㏖ N $O_2$- $h^{-l}$ $g^{-1}$ fr, wt.), total soluble protein (39.63mg $g^{-1}$ fr, wt.), NPR(16.66 $\mu$㏖ $m^{-2}$ $s^{-1}$ ), total soluble sugar (48.44 mg $g^{-1}$ fr. wt.), leaf yield/plant (0.689 kg) and shoot yield/plant (1.135 kg) showed its superiority over other tested varieties. Regression and correlation coefficients were analysed, and a strong positive correlation was found between NRA ＆ total soluble protein, NRA ＆ NPR, NRA ＆ total soluble sugar, NRA af unit leaf weight, NRA ＆ specific leaf weight, NRA ＆ leaf yield/plant, NRA ＆ shoot yield/plant, NPR ＆ leaf yield and NPR ＆ specific leaf weight.t.

• Journal of Sericultural and Entomological Science
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• v.9
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• pp.21-25
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• 1969

Various formulae for estimation of leaf production in mulberry trees were investigated and obtained. Four varieties of mulberry trees were used as the materials, and four characters. namely branch length (X, 1). branch diameter (X, 2). leaf number per branch (X, 3), and leaf area per branch (X, 4). were studies. The formulae to eatimate the leaf yield of mulberry trees are as follows: 1. Y$_1$v$_1$＝-115.760＋0.068X$_1$＋165.756X$_2$ Y$_1$v$_2$＝-221.500＋1.768X$_1$＋38.152X$_2$ Y$_1$v$_3$＝-253.826-0.116X$_1$＋289.507X$_2$ Y$_1$v$_4$＝ -157.559＋1.063X$_1$＋106.088X$_2$ where Y$_1$v$_1$, Y$_1$v$_2$, Y$_1$v$_3$, Y$_1$v$_4$, are showed the estimated yield of the each variety, namely Gaeryang souban, Ilchirye, Nosang. and Suwon Sang No. 4, respectively. X$_1$ and X$_2$ denote the measured values of branch length and branch diameter, respectively. 2. Y$\sub$7/v$_1$＝-118.478-0.665X$_1$＋184.445X$_2$＋2.346X$_3$ Y$\sub$7/v$_2$＝-217.432＋2.062X$_1$＋35.668X$_2$-1.058X$_3$ Y$\sub$7/v$_3$＝-206. 249-0.739X$_1$＋268.08X$_2$＋2.770X$_3$ Y$\sub$7/v$_4$＝-153.383＋0.009X$_1$＋2.024X$_2$＋0.171X$_3$where Y$\sub$7/v$_1$, Y$\sub$7/v$_2$, Y$\sub$7/v$_3$, Y$\sub$7/v$_4$, are the estimated yield of the each variety, namely Gaeryang. Souban, Ilichirye, Nosang, and Suwon Sang No. 4, respectively. X$_1$, X$_2$, X$_3$, denote the measured values of each character. branch length, branch diameter and leaf number per branch, respectively. 3. Y$\sub$11/v$_1$＝82. 567-1.283X$_1$＋15.501X$_2$＋0.640X$_3$＋3.511X$_4$ Y$\sub$11/v$_2$＝136.411＋0.311X$_1$＋1.921X$_2$-0. 217X$_3$＋0.214X$_4$ Y$\sub$11/v$_3$＝150.2Z7-0.139X$_1$＋11.788X$_2$＋0.143X$_3$＋0.381X$_4$ Y$\sub$11/v$_4$＝160.850＋0.323X$_1$＋66.076X$_2$-0.794X$_3$＋2..614X$_4$ where Y$\sub$11/v$_1$, Y$\sub$11/v$_2$, Y$\sub$11/v$_3$, Y$\sub$11/v$_4$, are the estimated yield values of four varieties, and X$_1$, X$_2$, X$_3$, X$_4$ denote the measured values of four characters. namely branch length, branch diameter. leaf number per branch and leaf area per branch. respectively. The estimation method of mulberry leaf yield by measurement of some characters, branch length. branch diameter. leaf number per branch and leaf area per branch. could be the better method to determine the leaf yield of mulberry trees without destroying the leaves and without weighting the leaves of mulberry trees than the other methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.75-86
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• 2020

The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAV multi-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effects according to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. The correlation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As a result of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, it is considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimate the damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations in early diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf area ratio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this study are expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.158-174
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• 2018

The leaf area index (LAI) is a major factor explaining the photosynthesis of vegetation, evapotranspiration, and energy exchange between the earth surface and atmosphere, and there have been studies on accurate and applicable LAI estimation methods. This study aimed to investigate the relationship between the actual LAI data, UAV image-based vegetation index, canopy height and satellite image (Sentinel-2) LAI and to present an effective LAI estimation method using UAV. As a result, among the six vegetation indices in this study, NDRE ($R^2=0.496$) and CIRE ($R^2=0.443$), which contained red-edge band, showed a high correlation. The application of the canopy height model data to the vegetation index improved the explanatory power of the LAI. In addition, in the case of NDVI, the saturation problem caused by the linear relationship with LAI was addressed. In this study, it was possible to estimate high resolution LAI using UAV images. It is expected that the applicability of such data will be improved if calibration and correction steps are carried out for various vegetation and seasonal images.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.815-820
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• 2015

For the establishment of effective water resources management platform for Jeju-Island, the characteristics, including surface runoff, evapotranspiration, groundwater recharge and discharge are to be properly quantified. Among these hydrologic components, interception due to vegetation is very important factor but it is hard to be quantified. After Von Hoyningen-Huene (1981) found the relationship between LAI (Leaf Area Index) and interception storage, LAI has been used for key factor to estimate interception and transpiration. In this study the equation suggested by Kozak et al. (2007) is implemented in SWAT-K (Soil and Water Assessment Tool - Korea) model and is tested at the Cheonmicheon watershed in Jeju-Island. The evaporation due to interception was estimated as 85~104mm, 8~11% of whole evaporation. Therefore it is necessary to consider the evaporation due to interception as a controlling factor to water budget of this watershed.

• Asian Journal of Atmospheric Environment
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• v.7 no.1
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• pp.17-24
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• 2013

Fog deposition onto the cool-temperate deciduous forest around Lake Mashu in northern Japan was estimated by the inferential method using the parameterizations of deposition velocity and liquid water content of fog (LWC). Two parameterizations of fog deposition velocity derived from field experiments in Europe and numerical simulations using a detailed multi-layer atmosphere-vegetation-soil model were tested. The empirical function between horizontal visibility (VIS) and LWC was applied to produce hourly LWC as an input data for the inferential method. Weekly mean LWC computed from VIS had a good correlation with LWC sampled by an active string-fog collector. By considering the enhancement of fog deposition due to the edge effect, fog deposition calculated by the inferential method using two parameterizations of deposition velocity agreed with that computed from throughfall data. The results indicated that the inferential method using the current parameterizations of deposition velocity and LWC can provide a rough estimation of water input due to fog deposition onto cool-temperature deciduous forests. Limitations of current parameterizations of deposition velocity related to wind speed, evaporation loss of rain and fog droplets intercepted by tree canopies, and leaf area index were discussed.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.556-563
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• 2015

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi-spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop $Circle^{TM}$) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

• Proceedings of the KSRS Conference
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• v.2
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• pp.543-546
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• 2006

NDVI has been very frequently used to estimate several biophysical parameters that are required for ecosystem models. Leaf area index (LAI), canopy closure, and biomass are among those biophysical parameters that are estimated by empirical relationship with NDVI. However, the type of remote sensing signals (raw DN value, at-sensor radiance, atmospherically corrected reflectance) used can vary the calculation of NDVI. In this study, we tried to attempt to compare the influence of NDVI linked with forest LAI for the watershed-scale ecosystem models to estimate evapotranspiration. Landsat ETM+ data were used to obtain various NDVI values over the study area in central Korea. The NDVI-based LAI and the resultant evapotranspiration estimation were greatly varied by the remote sensing signal applied.