• 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.

• Journal of Wetlands Research
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• v.26 no.2
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• pp.168-181
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• 2024

Constructed wetlands (CWs) deliver a range of ecosystem services, including the removal of contaminants, sequestration and storage of carbon, and enhancement of biodiversity. These services are facilitated through hydrological and ecological processes such as infiltration, adsorption, water retention, and evapotranspiration by plants and microorganisms. This study investigated the correlations between microbial populations, soil physicochemical properties, and treatment efficiency in a horizontal subsurface flow constructed wetland (HSSF CW) treating runoff from roads and parking lots. The methods employed included storm event monitoring, water quality analysis, soil sampling, soil quality parameter analysis, and microbial analysis. The facility achieved its highest pollutant removal efficiencies during the warm season (>15℃), with rates ranging from 33% to 74% for TSS, COD, TN, TP, and specific heavy metals including Fe, Zn, and Cd. Meanwhile, the highest removal efficiency was 35% for TOC during the cold season (≤15℃). These high removal rates can be attributed to sedimentation, adsorption, precipitation, plant uptake, and microbial transformations within the CW. Soil analysis revealed that the soil from HSSF CW had a soil organic carbon content 3.3 times higher than that of soil collected from a nearby landscape. Stoichiometric ratios of carbon (C), nitrogen (N), and phosphorus (P) in the inflow and outflow were recorded as C:N:P of 120:1.5:1 and 135.2:0.4:1, respectively, indicating an extremely low proportion of N and P compared to C, which may challenge microbial remediation efficiency. Additionally, microbial analyses indicated that the warm season was more conducive to microorganism growth, with higher abundance, richness, diversity, homogeneity, and evenness of the microbial community, as manifested in the biodiversity indices, compared to the cold season. Pollutants in stormwater runoff entering the HSSF CW fostered microbial growth, particularly for dominant phyla such as Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes, which have shown moderate to strong correlations with specific soil properties and changes in influent-effluent concentrations of water quality parameters.