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Study areaWith the advanced observational techniques, abundant data accumulation, and ability to carry on multi-scale experiments, the Huailai Remote Sensing Station and around (for short Huailai Station), located in Huailai, Hebei province, China (40.349°N, 115.785°E), becomes one of the ideal study areas for the observation and validation of the LAI27. The Huailai Station is mainly covered by corn and some weeds. So, we mainly use LAIS to monitor the growth cycle of corn (in April 2015, we submitted an application for plant collection permission to Huailai Remote Sensing Station and obtained approval.)Huailai WSN vegetation monitoring system includes 6 sets of monitoring equipment, and its distribution is shown in Fig. 1 as follows, in which red dot represents LAIS Node, purple frame represents MODIS pixel, red frame represents observation area. The observation system is designed for the application of remote sensing pixel scale authenticity tests. The observation scale is a 1 km MODIS pixel on the pixel scale, and the actual coverage area is 2 km * 1.5 km. The six sets of equipment cover the core area of the test station and the surrounding typical growth plot, which is a good representative of the 1 km pixel scale.Figure 1Equipment distribution of WSN vegetation monitoring network in Huailai (red dot represents LAIS Node; purple frame represents the footprint of a MODIS pixel.Full size imageEach piece of equipment consists of two cameras which were only one camera with two different angles in previous work23 set up at a height of 2.5–4 m above the ground (Fig. 2), one for vertical downward observation and the other for inclined observation, which can take canopy photos regularly every day at its fixed position. The observation system obtained the photos of the corn canopy from May to August, but the corn did not grow in August. Therefore, in this study, we selected the photos taken by the vertical observation camera of the corn sample plot in the experimental station from May to July 2015.Figure 2The design of the LAIS node.Full size imageRelated work—data acquisitionData collection using LAISThe data collection complies with the plant guidelines statement: “LAI-2000 Plant Canopy Analyzer Instrution Manual” (Supplementary Information 2) (https://www.licor.com/env/, Last visit time: 21 October 2021). Existing facilities such as the high poles and the wireless sensor network in the experimental station have proved convenient for the installation of the LAI measurement system. LAIS uses the GEO001 digital serial camera that is suitable for a variety of embedded image acquisition modes. The specification of the camera includes: the total field of view is 120°, the maximum image size is 2176 * 1920 (approximately 5 million pixels), mounted at a height of 3 m, the spatial resolution at ground level is about 3 mm. The acquired image is simultaneously stored in a flash card in two formats: the JPEG format merits in less file size thus suitable for quick wireless transfer; the RAW format, which is the user data in our analysis, contains 3 channel binary image in 10 bits bit-depth. Compared to our previous work, an important new feature of this camera is the programmable cut-off filter. As we know, unlike scientific sensor which has the precise spectral response to each band, the digital camera is cheap and can only acquire the so-called RGB image. Usual digital cameras have one NIR cut-off filter to exclude the near-infrared light. The GEO001 camera, which was a commercial camera produced by Zhongshan Yunteng Photographic Equipment Co., Ltd, has two cut-off filters: one is the NIR cut-off filter, another is a blue cut-off filter. Switching on the NIR cut-off filter results in an ordinary color image as in a usual household digital camera. While the blue cut-off filter is switched on and NIR cut-off filter is switched off, near-infrared light is allowed to reach the detector array and blue light is blocked, resulting in false-color images as in Fig. 3b. Adding near-infrared light can increase illumination in the shadow area, and blocking blue light can alleviate the disturbance of sun glint, so, switching to a blue cut-off filter helps to improve the image quality when the direct sunlight is strong such as around noon time.Figure 3Three images on July 2 of site 1: (a) and (c) are true-color images obtained at 05:31 a.m. and 6:32 p.m., and (b) is a false-color image when the blue filter is removed at 1:28 p.m.Full size imageTo acquire an image in the best illumination condition and avoid the influence of rain or other unsuitable weather, the image acquisition device based on WSN was set up to acquire images three times per day: 5:30 a.m., 1:30 p.m., and 6:30 p.m. According to our experience, when the canopy is open (sparse vegetation), usually images acquired at 6:30 p.m. are the best for classification because the direct sunlight is weak; when the canopy is closed (dense vegetation), the illumination on the soil background is very poor in all time, and classification is difficult. So, the camera is programmed to switch to a blue cut-off filter when acquiring images at 1:30 p.m., while the images acquired at other times were with NIR cut-off filter, resulting in true color images, as shown in Fig. 3.LAILLW data and LAI2000 dataTo evaluate the accuracy of the improved finite length averaging method proposed in this study, a field experiment was carried out to measure LAI by manual sampling (Supplementary Information 3,4). A field sampling scheme covering the corn growing season (late May to early July) was designed (Supplementary Information 1). The LAI of corn in the experimental area was measured by the quadrat harvesting method, and the validation data of LAI of corn in each growth period were obtained. Considering the rapid growth of the corn, the sampling experiment period was set as 1 week, but due to the actual work in summer and the influence of rainfall, six effective measurements were carried out in the field experiment: May 30, June 7, June 13, June 20, July 4 and July 16.The LAILLW method, which is also known as the shape factor method, involves outdoor and indoor measurements. The formulas are:$${text{L}} = {text{S}}*{text{N}}$$
(1)
$${text{f}} = {{text{S}} /{left( {sumlimits_{i = 1}^m {{text{len}}*{text{wid}}} } right)}}$$
(2)

where L represents the leaf area index, S refers to the area of a single plant, and N refers to the number of plants in a unit area. The shape factor ƒ is the ratio of the S to the value multiplied by the length and width of all leaves in the plant.To reduce measurement errors, 10 plants were selected in the sample, and the length and width of each leaf on each corn were recorded with a ruler. To obtain the shape factor, representative corn plants were cut next to the sample (not in the image coverage area) and the true area of each leaf was obtained by software, and the shape factor was derived from this23. Through the length and width of 10 strains measured in the field, and the shape factor obtained, the total leaf area of 10 corns can be calculated, and the average leaf area of one plant is finally obtained. The LAI value under the LAILLW method is obtained.Using the difference between the solar radiation values of the upper and lower canopies, the LAI2000 canopy analyzer can obtain LAI and set up a corresponding point folder to save the measured data for subsequent collation. 10 measurement points were selected for each site, and the average value was the final result for each site. To reduce the effects of the solar altitude angle on measurement accuracy, the experiments were repeated every two hours.To make it easier to record the date of data acquisition, the data were summarized in the order day of the year (DOY). For example, 30 May 2015 is the 150th day in the year and its DOY is 150. The DOY information of data acquisition using the LAILLW method and LAI2000 is specifically shown in Table 1.Table 1 The DOY information of data acquisition using the LAILLW and LAI2000.Full size tableMODIS LAI dataMODIS leaf area index data was downloaded from the United States Geological Survey (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php), named MCD15A2Hv006. It is an 8-day composite dataset with a 500-m pixel size. The algorithm chooses the best pixel available from all the acquisitions of both MODIS sensors located on NASA’s Terra and Aqua satellites from within the 8 days.In the comparison of MODIS LAI data, as the pixel of the satellite product is in 500 m resolution, it is not recommended to directly compare single node LAIS measurement with the MODIS LAI product because of the scale mismatch. Though complicated upscaling approaches have been discussed and implemented in Huailai station for other parameters28, it is not the purpose of this study So, we simply averaged the LAI in all the LAIS nodes to compare to the average MODIS LAI product in the 3 * 3 nearest pixels (1.5 km * 1.5 km), referred to as MODIS LAI_Mean in a later context, which approximately covers the area of all LAIS nodes. Time matching was carried out by selecting the date of the MODIS product closest to the date of the handheld LAI2000 measurement. The following Table 2 is obtained by taking 3 * 3 pixels closest to the LAIS Nodes.Table 2 MODIS leaf area index of 3 * 3 pixels around Huailai experimental station.Full size tableImproved LAIS methodsIn previous work, we have deployed sensors and cameras, and also have an automatic image processing and preliminary method of calculating LAI23. Figure 4 is a flow chart of our work. The previous articles focused on hardware and system implementation but did not pay much attention to performance. On this basis, we upgrade the image classification method and LAI calculation method, which will be explained in detail below.Figure 4Flow chart of leaf area index measurement system based on WSN.Full size imageImage preprocessing and classification methodsBecause of weather-related factors such as water vapor and dust or inaccurate exposure, a small number of the photographs are not clear. Besides, some of the image data cannot be decoded because of unstable communications and other factors. Therefore, it is necessary to check and select the photographs that meet the processing requirements before binary image processing. Currently, the selection process is carried out by human visual inspection based on the following principles: (1) when the canopy is open (sparse vegetation), the image at 6:30 p.m. is preferred, when the vegetation the canopy is closed (sparse vegetation), the image at 1:30 p.m. is preferred; (2) if the preferred image is not clear, other clear image acquired on the same day should be used; if all the images are not clear, then this day is marked as a failure.If we decided to use the image acquired at 1:30 p.m. It is also necessary to convert it from a false-color image to a true-color-like image (as shown in Fig. 3b) in which the leaves are shown in green color. The conversion is carried out by multiplying the vector of DN (digital number) of 3 bands with a coefficient matrix which is provided by the camera manufacturer. Another preprocessing is to choose the near nadir-view area of the image for further processing. As the off-nadir-view area of the image is subject to large geometric distortion as well as saturation of fraction of vegetation cover (FVC), they are not used in this study. The images are clipped to an ROI (region of interest) of about 2 * 2 square meters in ground area, with a maximum view zenith angle less than 30°.The study of the color spatial distributions of the crop images is helpful for the classification of the images and extraction of the image information. The color of the image pixel is the most direct and effective element that can be used to describe the image29. Because the red–green–blue (RGB) color space has the characteristic of a clear and convenient expression of information. When corn leaves are small, the crops in the fields are sparse, and most of them are soil background in the images. The soil in a lower hue is similar to the corn in terms of R and B components, while it has an overlap with the corn in G components when soil is in a higher hue. This makes it difficult to classify sparse corn scenes only by RGB space, so it is necessary to consider the characteristics of hue, luminosity, and saturation (HLS) spatial components.Statistical analysis showed that the component values of the crop leave in the RGB color space were in the ranges of G  > R and G  > B while the corresponding values for the soil follow the law that B  More

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Chemicals and materialsAnalytical standard ( > 99% purity) of spiromesifen, BSN2060-enol, and chromafenozide were purchased from AB Solution Co., Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea. HPLC grade water and acetonitrile were supplied by Merck, Darmstadt, Germany. QuEChERS kit (4.0 g magnesium sulfate, 1.0 g sodium chloride, 1.0 g sodium citrate tribasic dihydrate, 0.5 g disodium citrate sesquihydrate) were obtained from Phenomenex, California, USA. Individual stock solutions of the target compounds were prepared in acetonitrile and stored at − 20 °C before use.Field experimentsThe trials were carried out in a greenhouse farm during the season 2018 at two different sites (with approximately 24 km distance between both sites) located in Chuncheon and Hongcheon-gun, Gangwon-do, Republic of Korea following the method described by the Organization for Economic Co-operation and Development (OECD)38. The field test of lettuce (Latuca sativa L.) crop was conducted in Chuncheon city, and perilla (Perilla frutescens (L.) Britton) crop in Hongcheon city. The area of each field was divided into two plots (treatment and control). The treatment plots were further divided into three replicates (subplots 33 m2). The control plot was separated by a buffer zone of 3 m2 from the treated site. To minimize spray overlap, buffer zones (1 m) were set up between subplots. The commercial products of spiromesifen 20% SC diluted 2000 times and chromafenozide 5% EC diluted 1000 times were sprayed twice at 7-days intervals using an automatic sprayer. After the second spray samples (lettuce and perilla leaves) were collected from each subplot at 0 (2 h after spraying), 1, 3, 5, and 7 days according to the Korean RDA23 method. Thirty samples 1.0 kg each from the collected crop were placed in polyethylene bag and labeled. After collection, the samples were transported to the laboratory, where they were chopped and homogenized. The homogenized samples were kept frozen at − 20 °C until analysis.We confirm all plant samples used in the current work comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.Samples pretreatmentA QuEChERS method was used for the extraction of the targeted compounds from lettuce and perilla leaves. A 10 g of previously homogenized samples were weighed into a 50 mL polypropylene centrifuge tube and mixed with 10 mL of water followed by 10 mL of acetonitrile. The samples were shaken at 1500 rpm in a shaker machine for 10 min. Then commercial QuEChERS kit was added, and the mixtures were shaken vigorously for 2 min in a shaker. Subsequently, the samples were centrifuged at 3584 g-force for 10 min. After centrifugation, the supernatant was filtered with a 0.22 μm membrane filter and transferred into the glass vial for instrumental analysis.LC-MS/MS analysisQuantitative determination of the tested compounds was carried out by using HPLC system Dionex Ultimate 3000 (Thermo Science, USA) coupled with tandem mass spectrometry (MS/MS) (TSQ Quantum Access Max (Thermo Science, USA). Water (solvent A) and acetonitrile (solvent B) containing 0.1% formic acid and 5 mM ammonium format were used as mobile phase at a flow rate of 0.4 mL/min and injection volume 1.0 µL. To obtain desirable chromatographic peaks, two different instrumental conditions were used. The chromatographic separation of spiromesifen was separated by Capcell core-C18 (2.1 mm I.D. × 150 mm × 2.7 μm, Shiseido Co., Ltd., Tokyo, Japan) and BSN2060-enol was performed by C18 column (Poroshell 120 SB-Ag, 2.1 mm I.D. × 100 mm × 2.7-μm, Agilent Technologies, Santa Clara, California, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5.0%; 2.0 min 5%; 2.5 min, 95%; 6.0 min, 95%; 6.5 min, 5.0%; 10 min, 5.0%. Likewise, chromafenozide was separated by C18 column (Imtakt Unison UK-C18, 2.0 mm I.D. × 100 mm × 3.0-μm, Imtakt, Portland, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5%; 1.0 min, 5.0%; 1.5 min, 90%; 5.0 min, 90%; 7.0 min, 5.0%; 10 min, 5.0%. An MS/MS system (TSQ quantum ultra, Thermo Science, USA) equipped with an electrospray ionization source operating in positive mode (ESI+) was used. The MS/MS parameters and selected product ions are shown in supplementary Tables S2 and S3.The calculation of spiromesifen total residuesThe total residues in lettuce and perilla samples were calculated using Eq. (1)23.Total residues of spiromesifen (mg/kg) = spiromesifen + (BSN2060 residue × 1.36). The conversion factor was calculated as follow;$${1}.{36},{text{(conversion}},{text{factor)}} = frac{{370.49left( {{text{spiromesifen}},{text{MW}}} right)}}{{272.34{ }left( {{text{BSN}}2060,{text{MW}}} right)}}$$
(1)
where MW molecular weight.Initial deposition calculationThe initial residues of spiromesifen and chromafenozide deposition in lettuce and perilla leaves were calculated from 0-day according to Eq. (2) described by Kang et al.12 as follow;$${text{A }},({text{mg}}/{text{kg}}) = {text{B(mg}}/{text{kg)}} times frac{100}{{{text{C}}({text{% }})}} times frac{1}{{text{E}}} times 1000$$
(2)
A: Initial residue (mg/kg), B: Residues (mg/kg) on 0 day, C: active ingredients, E: dilution factor.Method validationThe analytical method was validated in terms of different performance criteria such as linearity, accuracy, precision, and method limit of quantitation (MLOQ). Matrix-matched standards were used to construct the calibration curve by evaporating (0.01, 0.05, 0.1, 0.2, 0.5, 0.7 and 1.0 mg/kg) working solution (1 mL) and re-dissolved in the extract of control sample. The linearity of the matrix-matched calibration curve was evaluated by the values of the correlation coefficient (R2). The accuracy and precision were obtained in terms of recovery (70–120%) and repeatability (n = 3). The recoveries were determined by spiking the analytes at two concentrations levels (0.1 mg/L) and (0.5 mg/L) in 10 g control samples and were quantified by comparing the response of analytes in samples with response in calibration standard solutions prepared in matrix. The repeatability expressed as the relative standard deviation (RSD) of the analyzed samples was calculated from three repetitions. The MLOQ was calculated by Eq. (3) taking into consideration the following factors: the instrument limit of detection, volume of extraction solvent, injection volume, dilution factor, and sample amount39,40.$${text{MLOQ}},{text{(mg}}/{text{kg)}} = {text{A(ng)}} times frac{{text{B(mL)}}}{{{text{C(}}upmu {text{L)}}}} times frac{{text{D}}}{{text{E(g)}}}$$
(3)
where A: instrument limit of detection, B: volume of extraction solvent, C: injection volume, D: dilution factor, E: sample amount.Half-life calculationThe dissipation patterns of spiromesifen and chromafenozide in lettuce and perilla leaves over time were found following the first-order kinetics model28. The half-life was determined by the following equation:$${text{C}}_{{text{t}}} = {text{C}}_{0} times {text{e}}^{{ – {text{kt}}}} ,{text{DT}}_{{{5}0}} = {text{ln2}}/{text{k}}$$where Ct is the concentration of the insecticide, C0 represents the initial residue concentration of insecticide, t is the time (days) after insecticide application, and k is the constant rate.Safety assessmentIn this study, the safety assessments (percent acceptable daily intake; %ADI) of the target insecticides that are consumed with lettuce and perilla leaves were calculated by the ratio of estimated daily intake (EDI) to acceptable daily intake (ADI). The EDI was calculated using insecticide concentration and average consumption of food commodities per person per day. In addition, the theoretical maximum daily intakes (TMDIs) of both insecticides were calculated using the maximum residue limits (MRLs) and average body weight (60 kg) of adults in Republic of Korea. TMDIs were calculated following the equation described by Kim et al.41.$$begin{aligned} & {text{ADI (mg}}/{text{person}}/{text{day)}} = {text{ADI}},({text{mg}}/{text{kg}}/{text{body weight}}/{text{day}}),{text{of target insecticide}} times {text{6}}0,({text{average body weight}}) \ & {text{EDI (mg}}/{text{kg}}/{text{person)}} = {text{concentration of target insecticide (mg}}/{text{kg)}} times {text{ daily food intake (g)}} \ & % {text{ADI}} = {text{EDI}}/{text{ADI}} times {text{1}}00 \ & {text{TMDI}}% = sum % {text{ADI of all registered crops}} \ end{aligned}$$ More

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