• Title/Summary/Keyword: nutrient management

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Deep Learning Approaches for Accurate Weed Area Assessment in Maize Fields (딥러닝 기반 옥수수 포장의 잡초 면적 평가)

  • Hyeok-jin Bak;Dongwon Kwon;Wan-Gyu Sang;Ho-young Ban;Sungyul Chang;Jae-Kyeong Baek;Yun-Ho Lee;Woo-jin Im;Myung-chul Seo;Jung-Il Cho
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.25 no.1
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    • pp.17-27
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    • 2023
  • Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

Investigation on the water quality challenges and benefits of buffer zone application to Yongdam reservoir, Republic of Korea (용담호의 홍수터 적용을 위한 문제점 및 이점 조사 연구)

  • Franz Kevin Geronimo;Hyeseon Choi;Minsu Jeon;Lee-Hyung Kim
    • Journal of Wetlands Research
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    • v.25 no.4
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    • pp.274-283
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    • 2023
  • Buffer zones, an example of nature-based solutions, offer wide range of environmental, social and economic benefits due to their multifunctionality when applied to watershed areas promoting blue-green connectivity. This study evaluated the effects of buffer zone application to the water quality of Yongdam reservoir tributaries. Particularly, the challenges and improvement of the buffer zone design were identified and suggested, respectively. Water and soil samples were collected from a total of six sites in Yongdam reservoir from September 2021 to April 2022. Water quality analyses revealed that among the sites monitored, downstream of Sangjeonmyeon Galhyeonri (SG_W_D2) was found to have the highest concentration for water quality parameters turbidity, total suspended solids (TSS), chemical oxygen demand (COD), total phosphorus (TP) and total nitrogen (TN). This finding was attributed to the algal bloom observed during the sampling conducted in September and October 2021. It was found through the soil analyses that high TN and TP concentrations were also observed in all the agricultural land uses implying that nutrient accumulation in agricultural areas are high. Highest TN concentration was found in the agricultural area of Jeongcheonmyeon Wolpyeongri (JW_S_A) followed by Jucheonmyeon Sinyangri (JS_S_A) while the lowest TN concentration was found in the original soil of Sangjeonmyeon Galhyeonri (SG_S_O). Among the types of buffer zones identified in this study, Type II-A, Type II-B and Type III were found to have blue-green connectivity. However, initially, blue-green connectivity in these buffer zone types were not considered leading to poor design and poor performance. As such, improvement in the design considering blue-green network and renovation must be considered to optimize the performance of these buffer zones. The findings in this study is useful for designing buffer zones in the future.

Soil Physical Properties of Arable Land by Land Use Across the Country (토지이용별 전국 농경지 토양물리적 특성)

  • Cho, H.R.;Zhang, Y.S.;Han, K.H.;Cho, H.J.;Ryu, J.H.;Jung, K.Y.;Cho, K.R.;Ro, A.S.;Lim, S.J.;Choi, S.C.;Lee, J.I.;Lee, W.K.;Ahn, B.K.;Kim, B.H.;Kim, C.Y.;Park, J.H.;Hyun, S.H.
    • Korean Journal of Soil Science and Fertilizer
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    • v.45 no.3
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    • pp.344-352
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    • 2012
  • Soil physical properties determine soil quality in aspect of root growth, infiltration, water and nutrient holding capacity. Although the monitoring of soil physical properties is important for sustainable agricultural production, there were few studies. This study was conducted to investigate the condition of soil physical properties of arable land according to land use across the country. The work was investigated on plastic film house soils, upland soils, orchard soils, and paddy soils from 2008 to 2011, including depth of topsoil, bulk density, hardness, soil texture, and organic matter. The average physical properties were following; In plastic film house soils, the depth of topsoil was 16.2 cm. For the topsoils, hardness was 9.0 mm, bulk density was 1.09 Mg $m^{-3}$, and organic matter content was 29.0 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.32 Mg $m^{-3}$, and organic matter content was 29.5 g $kg^{-1}$; In upland soils, depth of topsoil was 13.3 cm. For the topsoils, hardness was 11.3 mm, bulk density was 1.33 Mg $m^{-3}$, and organic matter content was 20.6 g $kg^{-1}$. For the subsoils, hardness was 18.8 mm, bulk density was 1.52 Mg $m^{-3}$, and organic matter content was 13.0 g $kg^{-1}$. Classified by the types of crop, soil physical properties were high value in a group of deep-rooted vegetables and a group of short-rooted vegetables soil, but low value in a group of leafy vegetables soil; In orchard soils, the depth of topsoil was 15.4 cm. For the topsoils, hardness was 16.1 mm, bulk density was 1.25 Mg $m^{-3}$, and organic matter content was 28.5 g $kg^{-1}$. For the subsoils, hardness was 19.8 mm, bulk density was 1.41 Mg $m^{-3}$, and organic matter content was 15.9 g $kg^{-1}$; In paddy soils, the depth of topsoil was 17.5 cm. For the topsoils, hardness was 15.3 mm, bulk density was 1.22 Mg $m^{-3}$, and organic matter content was 23.5 g $kg^{-1}$. For the subsoils, hardness was 20.3 mm, bulk density was 1.47 Mg $m^{-3}$, and organic matter content was 17.5 g $kg^{-1}$. The average of bulk density was plastic film house soils < paddy soils < orchard soils < upland soils in order, according to land use. The bulk density value of topsoils is mainly distributed in 1.0~1.25 Mg $m^{-3}$. The bulk density value of subsoils is mostly distributed in more than 1.50, 1.35~1.50, and 1.0~1.50 Mg $m^{-3}$ for upland and paddy soils, orchard soils, and plastic film house soils, respectively. Classified by soil textural family, there was lower bulk density in clayey soil, and higher bulk density in fine silty and sandy soil. Soil physical properties and distribution of topography were different classified by the types of land use and growing crops. Therefore, we need to consider the types of land use and crop for appropriate soil management.