Estimation of plant density and plant height of winter wheat breeding material based on UAV digital images

Estimation of plant density and plant height of winter wheat breeding material based on UAV digital images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimation of plant density and plant height of winter wheat breeding material based on UAV digital images hecang zang, WANG Yanjing, YANG Xiuzhong, HE Jia, ZHAO Qing, zhou meng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1469702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 【 Aims 】 In order to obtain the plant density and plant height information of winter wheat breeding material quickly and accurately, it was of great practical significance for the growth monitoring and yield prediction of winter wheat breeding material. 【Method】 In this study, a drone equipped with a digital camera was used to obtain the drone digital orthophoto (DOM) and digital surface model (DSM) of winter wheat breeding material at the seedling stage, jointing stage, booting stage, flowering stage and grain filling stage. The coverage of winter wheat was extracted from drone images, and the relationship between coverage and plant density was established; the plant height estimation models of winter wheat breeding material were established respectively at jointing, booting, flowering and grain filling stages. Based on the ground measurement of winter wheat breeding material height (H), the accuracy of plant height of winter wheat breeding material extracted by DSM was verified. 【Result】 The results showed that the winter wheat coverage extracted based on the UAV images at the seedling stage had a high correlation with the measured plant density, and the R 2 was 0.8205. The new winter wheat cultivar H extracted by DSM was significantly correlated with the measured H, and the fitted R 2 and RMSE of the predicted plant height and the measured value were 0.9554 and 6.3233 cm, respectively. 【Conclusion】 The results indicated that the use of UAV aerial imagery to predict the plant density and plant height of winter wheat breeding material has good applicability, and can provide technical reference for future crop phenotype information monitoring. Unmanned aerial vehicle digital image winter wheat breeding material plant density Plant height Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Henan Province is the main wheat production area in my country. In the past five years, the planting area had stabilized at more than 5.67 million hectares, accounting for nearly 1/4 of the country's total wheat planting area, with a total output of 37.532 billion kilograms. It was of great significance to stabilizing my country's food security [ 1 – 3 ]. In the process of wheat breeding, the acquisition of phenotypic information was the bottleneck of current wheat breeding research, and the efficiency of phenotypic information acquisition was the key factor restricting breeding research. Plant height was a key indicator for dynamically measuring crop health and overall growth status, and was widely used to estimate crop biological yield and final grain yield [ 4 ]. At present, the common method for obtaining plant height information of new wheat varieties in the field was that researchers manually measure the specific plant height parameters of each breeding plot. However, the manual measurement method had strong subjectivity, strong randomness, and lack of uniform standards, which leaded to time-consuming, labor-intensive and low-efficiency of breeding researchers [ 5 – 7 ]. With the rapid development of machine vision technology and artificial intelligence technology, high-throughput crop phenotyping technology and crop phenotyping research were effective ways to solve this dilemma. Therefore, the use of UAV remote sensing technology could quickly and accurately obtain the plant density and plant height information of new wheat varieties, which was of great practical significance for the growth monitoring and yield prediction of new wheat varieties. In recent years, drones equipped with high-resolution digital cameras had enabled the development of modern agriculture to quantify. Due to its non-destructiveness, high efficiency and low cost, it had been widely used in crop height monitoring [ 8 – 9 ]. Scholars at home and abroad had used UAV remote sensing to extract crop plant height with digital image processing technology [ 10 – 12 ]. Xie Tianjin et al. [ 13 ] researched that the use of low-altitude UAV remote sensing platform equipped with high-definition digital cameras could quickly obtain rapeseed plant height, rape plant height had better prediction accuracy. Liu Yang et al. [ 14 ] reported that the digital surface model of the experimental field was generated based on UAV hyperspectral images combined with ground control points, and the extraction of potato plant height and the measured plant height had a high degree of fit. Shu Meiyan et al. [ 15 ] showed that the UAV digital images and watershed algorithm were used to segment the structural dataset of citrus canopy, and then the canopy height model of citrus trees was established to extract the plant height using digital surface module. Wang Wei et al. [ 16 ] used a UAV equipped with a Zenmuse X4S camera, extracted wheat coverage based on UAV images, and established the relationship between wheat coverage and plant density, so as to achieve rapid acquisition of wheat plant density. At present, there were few research reports on the estimation of plant density and plant height of winter wheat breeding material based on UAV images. In this study, winter wheat breeding material were taken as the research object, the field measured plant height was used to verify the accuracy, and to provide a rapid, non-destructive and high-throughput field monitoring technical means for the prediction of plant density and plant height of winter wheat breeding material. Materials And Methods 1.1 Overview of the test site From 2020 to 2021, the experiment will be carried out in the winter wheat breeding experimental site of the Henan Modern Agricultural Research and Development Base of the Henan Academy of Agricultural Sciences. The specific location information of the experimental site was shown in Fig. 1. The test site was located at 35°0' north latitude and 113°41' east longitude, the climate type was warm temperate continental monsoon climate, with an annual average temperature of 14.4°C, an annual average rainfall of 549.9 mm, and an annual sunshine hours of 2300 ~ 2600 h, the winter wheat-corn rotation was the main planting pattern in this area. The soil total nitrogen content of the 20 cm plough layer of the tested soil was 0.87 g.kg − 1 , total phosphorus content was 0.45 g.kg − 1 , organic matter content was 15.98 g.kg − 1 , ammonium nitrogen was 0.17 mg.kg − 1 , and nitrate nitrogen was 7.15 mg.kg − 1 , available phosphorus 22.80 mg.kg − 1 . The experiment was arranged in random blocks, the sowing date was October 9, 2020, the planting density was 1.95 million plants/hm 2 , and there were 501 plots in total. Each plot was planted with 6 rows of winter wheat breeding material, repeated 3 times, and the plot area was 12 m 2 . The management measures of the experimental field were higher than those of the ordinary field. 1.2 Ground data acquisition In the seedling stage (November 10, 2020), two 0.5 meter sample points were randomly selected in each plot, and the plant density of winter wheat breeding material was calculated by manual survey. Four key growth periods were acquired at the jointing stage (March 31, 2021), booting stage (April 19, 2021), flowering stage (April 29, 2021) and grain filling stage (May 22, 2021). Plant height data of new wheat varieties. Measurement method of wheat plant height data: randomly select 5 wheat plants around and in the middle of each plot, and measure the wheat plant height with a ruler. 1.3 Data acquisition of UAV digital image Consistent with the ground data collection time, high-definition digital images of new wheat varieties were obtained respectively in key growth periods. The test used the DJI four-rotor electric drone Phantom 4 Pro, with a body weight of about 1380 g, a maximum flight speed of 20 m/s, and a battery life of 20 minutes. The camera's image sensor was a 1/2.3 inch CMOS with 20 million effective pixels. The image collection time was 10:00 in the morning, and the weather was clear and cloudless. During the aerial photography, the drone flew at a height of 25 m, with a flight speed of 3 m/s, and the heading overlap and side overlap were both 80%. The flight adopted the route planned by automatic takeoff, and a total of 5 routes are planned. After the aerial photography was completed, the method of automatic return was used to land. Due to the influence of weather conditions, the flight route may be slightly shifted during flight, and the number of images collected each time will be different, as shown in Table 1. 1.4 Data processing of digital image 1.4.1 Establishment of crop surface model Firstly, the obtained digital images of new wheat varieties were screened in different growth periods, and the images with spatial position information were imported into the Pix4D mapper software, and then image stitching, radiation correction, point cloud and texture information were performed, and finally the UAV digital image was generated DOM and DSM, the image stitching process was shown in Fig. 2. 1.4.2 Wheat plant height extraction based on DSM Combined with ground control points (GCP) and using Pix4D mapper software, the obtained UAV digital images of new wheat varieties were spliced and processed to generate DSM of new wheat varieties at jointing stage, booting stage, flowering stage and grain filling stage. High-definition digital images of new wheat varieties at different growth stages were spliced to generate DSM, which bare field, jointing stage, booting stage, flowering stage and grain filling stage were marked as DSM 0 , DSM 1 , DSM 2 , DSM 3 , and DSM 4 . Since the bare land DSM 0 was the image of the bare land after sowing, which regarded as the ground surface datum, and it was used to extract the plant height (H i ) method of the difference between the wheat DSM i and DSM 0 in each growth period, as shown in Fig. 3. The formula was as follows: H i =DSM i -DSM 0 , i = 1, 2, 3, 4. 1.5 Statistical analysis The coefficient of determination (R 2 ) and the root mean square error (RMSE) were selected as the indicators for evaluating the estimated model and the validation model. The R 2 was larger, the model was better; the RMSE was smaller, the estimation accuracy of the model was higher. Results And Analysis 2.1 Plot identification based on orthophotos Pix4D mapper software was used to stitch the UAV digital images obtained from the experimental field of new wheat varieties to accurately generate digital orthophotos. It could be seen from Fig. 4 that a total of 501 plots were extracted from the new wheat variety experimental field of new wheat variety using the orthophoto image. At the same time, it could be clearly observed that the boundaries of the new wheat varieties were obvious, the growth of each plot was uniform, and the leaf color difference between the plots was obvious. 2.2 Estimation of wheat plant density based on coverage Based on the image processing method, the coverage of new wheat varieties was calculated and analyzed with the measured plant density of new wheat varieties. The results were shown in Fig. 5. There was a good correlation between coverage and plant density, R 2 reached 0.8205, indicating that the method of using coverage to estimate wheat plant density is feasible and has high accuracy. It showed that the method of estimating wheat plant density using coverage was feasible and had high accuracy. 2.3 Extraction of plant height and ground measurement based on DSM It could be seen from Fig. 5 that from the jointing stage to the flowering stage, the measured plant height of the new wheat variety on the ground and the wheat plant height extracted by DSM both showed a linear and rapid growth trend. After the flowering stage, the change trend of the plant height was basically constant; the plant height increased the fastest at the flowering stage, and the grain-filling stage decreased, because the grains of winter wheat gradually matured and full after entering the grain-filling stage, and the quality of the wheat ears increased bending, which result to a shorter plant height. During the whole growth period, the plant height extracted based on DSM was lower than the measured plant height. 2.4 Extraction of plant height of wheat based on DSM According to DSM combined with GCP information, H dsm1 , H dsm2 , H dsm3 , and H dsm4 corresponding to the jointing stage, booting stage, flowering stage and grain filling stage were obtained, and the distribution of the results was shown in Fig. 6. 501 new wheat varieties could be identified in each growth period, and a total of 2004 wheat plant height data were obtained in four growth periods. During the whole growth period, the plant height effect of new wheat varieties extracted based on DSM was better. The analysis of the extracted wheat plant height and the measured wheat plant height showed that the R 2 and RMSE were 0.9554 and 6.3233 cm, it showed that the new wheat variety extracted by DSM had higher plant height. Discussion And Conclusions In recent years, with the application of UAV technology in field crop research, synchronous monitoring of large-scale crops could be achieved, and high-precision and repeatable crop height data can be obtained [ 17 ]. As the main method for obtaining plant height of crops, the UAV platform had mainly focused on the growth information and phenotypic traits of high-stalk crop cultivation in the field [ 18 – 22 ]; however, there were relatively few study on the growth information and phenotypic parameters of new dwarf crop varieties. It could be seen from the experimental results that there was a good correlation between plant density and coverage of winter wheat breeding material at seedling stage, and R 2 reached 0.8205. It showed that the method of estimating wheat plant density using coverage was feasible and had a high precision. Remote sensing altimetry generally obtained the natural plant height of field crops in natural state, which was different from agronomic measurement of plant length; when the natural plant height of the crop was different from the real plant length, which can try to use multiple sensors to synergistically extract the plant height. Liu Zhikai et al. [ 23 ] showed that using the visible light image acquisition system of unmanned aerial vehicles, high-definition digital images of winter wheat from jointing stage to maturity of winter wheat were obtained, DOM and DSM were established, and the model was validated. Tao Huilin et al. [ 24 ] pointed out that the DSM of winter wheat was generated based on high-definition digital images of drones, and the plant height of winter wheat was extracted by using DSM. Yan An et al. [ 25 ] reported that a low-altitude remote sensing platform was formed with a UAV equipped with a high-definition digital camera to obtain images of cotton varieties in the flowering and boll stage; splicing software and high-definition digital images were used to generate DOM and DSM in the study area, and cotton plant height was extracted. Guo Tao et al. [ 26 ] researched that the plant height estimation models of wheat varieties were constructed respectively based on the DOM and DSM at different growth stages. In this study, a total of 501 new winter wheat cultivars DSM were obtained at the jointing stage, booting stage, flowering stage and grain filling stage, and the plant heights of new wheat cultivars were extracted, and 100 measured plant heights were fitted with the plant heights extracted by DSM. The fitted R 2 and RMSE of the predicted and measured plant heights were 0.9554 and 6.3233 cm respectively. The showed that the plant height of winter wheat breeding material extracted had higher accuracy based on DSM, which could provide theoretical basis and technical reference for winter wheat yield prediction. Declarations Acknowledgment This study has been funded by key science and technology projects in Henan Province (Contract Number: 212102110253), Henan Academy of Agricultural Sciences Independent Innovation Project (Contract Number: 2022ZC51) and science and technology innovation leading talent cultivation program of the Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences (Contract Number: 2022KJCX02). References The people’s government of Henan province. Wheat quality was good enough this year in Henan province [N].Henan Daily, 2020-11-05(3). Henan Statistics Bureau (2020) Henan general team of investigation under the NBS. Henan statistical yearbook[M]. 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J Triticeae Crops 40(9):1129–1140 Tables Table 1 Digital image acquisition time and quantity of UAV in winter wheat under different growth and developement stages Growing and development stage Shooting time Number of digital images Seedling stage November 10, 2020 970 Jointing stage March 31, 2021 970 Booting stage April 19, 2021 972 Flowering stage April 29, 2021 972 Filling stage May 22, 2021 973 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1469702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":97631533,"identity":"d4f4ae95-b2f6-4310-87ec-52421cf1ad5f","order_by":0,"name":"hecang 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15:14:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41132,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the coverage and the measured plant density\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1469702/v1/89a1687c2ede0db1825bf2d3.png"},{"id":20347686,"identity":"e898eb99-6f98-4c2d-a1d6-7a0b785f0940","added_by":"auto","created_at":"2022-04-14 15:14:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":13078,"visible":true,"origin":"","legend":"\u003cp\u003eWheat plant height measured on the ground and extracted by DSM\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-1469702/v1/d35b571625f8fd9f72bbc197.png"},{"id":20347684,"identity":"b2a47630-b381-49d6-933b-2ae0b6db556e","added_by":"auto","created_at":"2022-04-14 15:14:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":733119,"visible":true,"origin":"","legend":"\u003cp\u003eHeight extracted from DSM of wheat breeding materials\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-1469702/v1/72c4fa3d9c5f2917bf4ba320.png"},{"id":22261671,"identity":"d2b81c02-21f7-433c-9dca-a7d4dfd5f0f3","added_by":"auto","created_at":"2022-06-05 01:32:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2600931,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1469702/v1/8daa0e92-8e48-4879-bab1-1eaa6e1c3047.pdf"}],"financialInterests":"","formattedTitle":"Estimation of plant density and plant height of winter wheat breeding material based on UAV digital images","fulltext":[{"header":"Background","content":"\u003cp\u003eHenan Province is the main wheat production area in my country. In the past five years, the planting area had stabilized at more than 5.67\u0026nbsp;million hectares, accounting for nearly 1/4 of the country\u0026apos;s total wheat planting area, with a total output of 37.532\u0026nbsp;billion kilograms. It was of great significance to stabilizing my country\u0026apos;s food security [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the process of wheat breeding, the acquisition of phenotypic information was the bottleneck of current wheat breeding research, and the efficiency of phenotypic information acquisition was the key factor restricting breeding research. Plant height was a key indicator for dynamically measuring crop health and overall growth status, and was widely used to estimate crop biological yield and final grain yield [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. At present, the common method for obtaining plant height information of new wheat varieties in the field was that researchers manually measure the specific plant height parameters of each breeding plot. However, the manual measurement method had strong subjectivity, strong randomness, and lack of uniform standards, which leaded to time-consuming, labor-intensive and low-efficiency of breeding researchers [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the rapid development of machine vision technology and artificial intelligence technology, high-throughput crop phenotyping technology and crop phenotyping research were effective ways to solve this dilemma. Therefore, the use of UAV remote sensing technology could quickly and accurately obtain the plant density and plant height information of new wheat varieties, which was of great practical significance for the growth monitoring and yield prediction of new wheat varieties.\u003c/p\u003e\n\u003cp\u003eIn recent years, drones equipped with high-resolution digital cameras had enabled the development of modern agriculture to quantify. Due to its non-destructiveness, high efficiency and low cost, it had been widely used in crop height monitoring [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Scholars at home and abroad had used UAV remote sensing to extract crop plant height with digital image processing technology [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Xie Tianjin et al. [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] researched that the use of low-altitude UAV remote sensing platform equipped with high-definition digital cameras could quickly obtain rapeseed plant height, rape plant height had better prediction accuracy. Liu Yang et al. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that the digital surface model of the experimental field was generated based on UAV hyperspectral images combined with ground control points, and the extraction of potato plant height and the measured plant height had a high degree of fit. Shu Meiyan et al. [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] showed that the UAV digital images and watershed algorithm were used to segment the structural dataset of citrus canopy, and then the canopy height model of citrus trees was established to extract the plant height using digital surface module. Wang Wei et al. [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e] used a UAV equipped with a Zenmuse X4S camera, extracted wheat coverage based on UAV images, and established the relationship between wheat coverage and plant density, so as to achieve rapid acquisition of wheat plant density. At present, there were few research reports on the estimation of plant density and plant height of winter wheat breeding material based on UAV images. In this study, winter wheat breeding material were taken as the research object, the field measured plant height was used to verify the accuracy, and to provide a rapid, non-destructive and high-throughput field monitoring technical means for the prediction of plant density and plant height of winter wheat breeding material.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e1.1 Overview of the test site\u003c/p\u003e\n\u003cp\u003eFrom 2020 to 2021, the experiment will be carried out in the winter wheat breeding experimental site of the Henan Modern Agricultural Research and Development Base of the Henan Academy of Agricultural Sciences. The specific location information of the experimental site was shown in Fig.\u0026nbsp;1. The test site was located at 35\u0026deg;0\u0026apos; north latitude and 113\u0026deg;41\u0026apos; east longitude, the climate type was warm temperate continental monsoon climate, with an annual average temperature of 14.4\u0026deg;C, an annual average rainfall of 549.9 mm, and an annual sunshine hours of 2300\u0026thinsp;~\u0026thinsp;2600 h, the winter wheat-corn rotation was the main planting pattern in this area. The soil total nitrogen content of the 20 cm plough layer of the tested soil was 0.87 g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, total phosphorus content was 0.45 g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, organic matter content was 15.98 g.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, ammonium nitrogen was 0.17 mg.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and nitrate nitrogen was 7.15 mg.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, available phosphorus 22.80 mg.kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The experiment was arranged in random blocks, the sowing date was October 9, 2020, the planting density was 1.95\u0026nbsp;million plants/hm\u003csup\u003e2\u003c/sup\u003e, and there were 501 plots in total. Each plot was planted with 6 rows of winter wheat breeding material, repeated 3 times, and the plot area was 12 m\u003csup\u003e2\u003c/sup\u003e. The management measures of the experimental field were higher than those of the ordinary field.\u003c/p\u003e\n\u003cp\u003e1.2 Ground data acquisition\u003c/p\u003e\n\u003cp\u003eIn the seedling stage (November 10, 2020), two 0.5 meter sample points were randomly selected in each plot, and the plant density of winter wheat breeding material was calculated by manual survey. Four key growth periods were acquired at the jointing stage (March 31, 2021), booting stage (April 19, 2021), flowering stage (April 29, 2021) and grain filling stage (May 22, 2021). Plant height data of new wheat varieties. Measurement method of wheat plant height data: randomly select 5 wheat plants around and in the middle of each plot, and measure the wheat plant height with a ruler.\u003c/p\u003e\n\u003cp\u003e1.3 Data acquisition of UAV digital image\u003c/p\u003e\n\u003cp\u003eConsistent with the ground data collection time, high-definition digital images of new wheat varieties were obtained respectively in key growth periods. The test used the DJI four-rotor electric drone Phantom 4 Pro, with a body weight of about 1380 g, a maximum flight speed of 20 m/s, and a battery life of 20 minutes. The camera\u0026apos;s image sensor was a 1/2.3 inch CMOS with 20\u0026nbsp;million effective pixels. The image collection time was 10:00 in the morning, and the weather was clear and cloudless. During the aerial photography, the drone flew at a height of 25 m, with a flight speed of 3 m/s, and the heading overlap and side overlap were both 80%. The flight adopted the route planned by automatic takeoff, and a total of 5 routes are planned. After the aerial photography was completed, the method of automatic return was used to land. Due to the influence of weather conditions, the flight route may be slightly shifted during flight, and the number of images collected each time will be different, as shown in Table\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e1.4 Data processing of digital image\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e1.4.1 Establishment of crop surface model\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003eFirstly, the obtained digital images of new wheat varieties were screened in different growth periods, and the images with spatial position information were imported into the Pix4D mapper software, and then image stitching, radiation correction, point cloud and texture information were performed, and finally the UAV digital image was generated DOM and DSM, the image stitching process was shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003e1.4.2 Wheat plant height extraction based on DSM\u003c/p\u003e\n\u003cp\u003eCombined with ground control points (GCP) and using Pix4D mapper software, the obtained UAV digital images of new wheat varieties were spliced and processed to generate DSM of new wheat varieties at jointing stage, booting stage, flowering stage and grain filling stage. High-definition digital images of new wheat varieties at different growth stages were spliced to generate DSM, which bare field, jointing stage, booting stage, flowering stage and grain filling stage were marked as DSM\u003csub\u003e0\u003c/sub\u003e, DSM\u003csub\u003e1\u003c/sub\u003e, DSM\u003csub\u003e2\u003c/sub\u003e, DSM\u003csub\u003e3\u003c/sub\u003e, and DSM\u003csub\u003e4\u003c/sub\u003e. Since the bare land DSM\u003csub\u003e0\u003c/sub\u003e was the image of the bare land after sowing, which regarded as the ground surface datum, and it was used to extract the plant height (H\u003csub\u003ei\u003c/sub\u003e) method of the difference between the wheat DSM\u003csub\u003ei\u003c/sub\u003e and DSM\u003csub\u003e0\u003c/sub\u003e in each growth period, as shown in Fig. 3. The formula was as follows: H\u003csub\u003ei\u003c/sub\u003e=DSM\u003csub\u003ei\u003c/sub\u003e-DSM\u003csub\u003e0\u003c/sub\u003e, i\u0026thinsp;=\u0026thinsp;1, 2, 3, 4.\u003c/p\u003e\n\u003cp\u003e1.5 Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and the root mean square error (RMSE) were selected as the indicators for evaluating the estimated model and the validation model. The R\u003csup\u003e2\u003c/sup\u003e was larger, the model was better; the RMSE was smaller, the estimation accuracy of the model was higher.\u003c/p\u003e"},{"header":"Results And Analysis","content":"\u003cp\u003e2.1 Plot identification based on orthophotos\u003c/p\u003e\n\u003cp\u003ePix4D mapper software was used to stitch the UAV digital images obtained from the experimental field of new wheat varieties to accurately generate digital orthophotos. It could be seen from Fig.\u0026nbsp;4 that a total of 501 plots were extracted from the new wheat variety experimental field of new wheat variety using the orthophoto image. At the same time, it could be clearly observed that the boundaries of the new wheat varieties were obvious, the growth of each plot was uniform, and the leaf color difference between the plots was obvious.\u003c/p\u003e\n\u003cp\u003e2.2 Estimation of wheat plant density based on coverage\u003c/p\u003e\n\u003cp\u003eBased on the image processing method, the coverage of new wheat varieties was calculated and analyzed with the measured plant density of new wheat varieties. The results were shown in Fig.\u0026nbsp;5. There was a good correlation between coverage and plant density, R\u003csup\u003e2\u003c/sup\u003e reached 0.8205, indicating that the method of using coverage to estimate wheat plant density is feasible and has high accuracy. It showed that the method of estimating wheat plant density using coverage was feasible and had high accuracy.\u003c/p\u003e\n\u003cp\u003e2.3 Extraction of plant height and ground measurement based on DSM\u003c/p\u003e\n\u003cp\u003eIt could be seen from Fig.\u0026nbsp;5 that from the jointing stage to the flowering stage, the measured plant height of the new wheat variety on the ground and the wheat plant height extracted by DSM both showed a linear and rapid growth trend. After the flowering stage, the change trend of the plant height was basically constant; the plant height increased the fastest at the flowering stage, and the grain-filling stage decreased, because the grains of winter wheat gradually matured and full after entering the grain-filling stage, and the quality of the wheat ears increased bending, which result to a shorter plant height. During the whole growth period, the plant height extracted based on DSM was lower than the measured plant height.\u003c/p\u003e\n\u003cp\u003e2.4 Extraction of plant height of wheat based on DSM\u003c/p\u003e\n\u003cp\u003eAccording to DSM combined with GCP information, H\u003csub\u003edsm1\u003c/sub\u003e, H\u003csub\u003edsm2\u003c/sub\u003e, H\u003csub\u003edsm3\u003c/sub\u003e, and H\u003csub\u003edsm4\u003c/sub\u003e corresponding to the jointing stage, booting stage, flowering stage and grain filling stage were obtained, and the distribution of the results was shown in Fig. 6. 501 new wheat varieties could be identified in each growth period, and a total of 2004 wheat plant height data were obtained in four growth periods. During the whole growth period, the plant height effect of new wheat varieties extracted based on DSM was better. The analysis of the extracted wheat plant height and the measured wheat plant height showed that the R\u003csup\u003e2\u003c/sup\u003e and RMSE were 0.9554 and 6.3233 cm, it showed that the new wheat variety extracted by DSM had higher plant height.\u003c/p\u003e"},{"header":"Discussion And Conclusions","content":"\u003cp\u003eIn recent years, with the application of UAV technology in field crop research, synchronous monitoring of large-scale crops could be achieved, and high-precision and repeatable crop height data can be obtained [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. As the main method for obtaining plant height of crops, the UAV platform had mainly focused on the growth information and phenotypic traits of high-stalk crop cultivation in the field [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]; however, there were relatively few study on the growth information and phenotypic parameters of new dwarf crop varieties. It could be seen from the experimental results that there was a good correlation between plant density and coverage of winter wheat breeding material at seedling stage, and R\u003csup\u003e2\u003c/sup\u003e reached 0.8205. It showed that the method of estimating wheat plant density using coverage was feasible and had a high precision.\u003c/p\u003e\n\u003cp\u003eRemote sensing altimetry generally obtained the natural plant height of field crops in natural state, which was different from agronomic measurement of plant length; when the natural plant height of the crop was different from the real plant length, which can try to use multiple sensors to synergistically extract the plant height. Liu Zhikai et al. [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] showed that using the visible light image acquisition system of unmanned aerial vehicles, high-definition digital images of winter wheat from jointing stage to maturity of winter wheat were obtained, DOM and DSM were established, and the model was validated. Tao Huilin et al. [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] pointed out that the DSM of winter wheat was generated based on high-definition digital images of drones, and the plant height of winter wheat was extracted by using DSM. Yan An et al. [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] reported that a low-altitude remote sensing platform was formed with a UAV equipped with a high-definition digital camera to obtain images of cotton varieties in the flowering and boll stage; splicing software and high-definition digital images were used to generate DOM and DSM in the study area, and cotton plant height was extracted. Guo Tao et al. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] researched that the plant height estimation models of wheat varieties were constructed respectively based on the DOM and DSM at different growth stages. In this study, a total of 501 new winter wheat cultivars DSM were obtained at the jointing stage, booting stage, flowering stage and grain filling stage, and the plant heights of new wheat cultivars were extracted, and 100 measured plant heights were fitted with the plant heights extracted by DSM. The fitted R\u003csup\u003e2\u003c/sup\u003e and RMSE of the predicted and measured plant heights were 0.9554 and 6.3233 cm respectively. The showed that the plant height of winter wheat breeding material extracted had higher accuracy based on DSM, which could provide theoretical basis and technical reference for winter wheat yield prediction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e \u003cp\u003eThis study has been funded by key science and technology projects in Henan Province (Contract Number: 212102110253), Henan Academy of Agricultural Sciences Independent Innovation Project (Contract Number: 2022ZC51) and science and technology innovation leading talent cultivation program of the Institute of Agricultural Economics and Information, Henan Academy of Agricultural Sciences (Contract Number: 2022KJCX02).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eThe people\u0026rsquo;s government of Henan province. 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Remote Sens 9(2):111\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYang Q, Ye H, Huang K et al (2017) Estimation of leaf area index of sugarcane using crop surface model based on UAV image [J]. Trans Chin Soc Agricultural Eng (Transactions CSAE) 33(8):104\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNIU Q, L, FENG H K, YANG G J et al (2018) Monitoring plant height and leaf area index of maize breeding material based on UAV digital images [J]. Trans Chin Soc Agricultural Eng (Transactions CSAE) 34(5):73\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiu ZK, Niu Y, Wang Y, Han WT (2019) Estimation of plant height of winter wheat based on UAV visible image [J]. J Triticeae Crops 39(7):859\u0026ndash;866\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTao HL, Xu LJ, Feng HK, Yang GJ, Yang XD, Miao MK, Dai Y (2019) Estimation of plant height and biomass of winter wheat based on UAV digital image [J]. Trans Chin Soc Agricultural Eng (Transactions CSAE) 35(19):107\u0026ndash;116\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYan A, Guo T, Chen QJ, Geng HW, Guo B, Sun FL (2020) Prediction of Cotton Plant Height Based on UAV Image [J]. Xinjiang Agricultural Sciences 57(8):1493\u0026ndash;1502\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGuo T, Yan A, Geng HW (2020) Prediction of wheat plant height and leaf area index based on UAV image [J]. J Triticeae Crops 40(9):1129\u0026ndash;1140\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Digital image acquisition time and quantity of UAV in winter wheat under different growth and developement stages\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eGrowing and development stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003eShooting time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003eNumber of digital images\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eSeedling stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; November 10, 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003e970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eJointing stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003e\u0026nbsp; March 31, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003e970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eBooting stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003e\u0026nbsp;April 19, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eFlowering stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003e\u0026nbsp;April 29, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"34.50704225352113%\"\u003e\n \u003cp\u003eFilling stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"36.61971830985915%\"\u003e\n \u003cp\u003eMay 22, 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.87323943661972%\"\u003e\n \u003cp\u003e973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Unmanned aerial vehicle, digital image, winter wheat breeding material, plant density, Plant height","lastPublishedDoi":"10.21203/rs.3.rs-1469702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1469702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003e【\u003c/strong\u003eAims\u003cstrong\u003e】 \u003c/strong\u003eIn order to obtain the plant density and plant height information of winter wheat breeding material quickly and accurately, it was of great practical significance for the growth monitoring and yield prediction of winter wheat breeding material. \u003c/p\u003e\u003cp\u003e【Method】 In this study, a drone equipped with a digital camera was used to obtain the drone digital orthophoto (DOM) and digital surface model (DSM) of winter wheat breeding material at the seedling stage, jointing stage, booting stage, flowering stage and grain filling stage. The coverage of winter wheat was extracted from drone images, and the relationship between coverage and plant density was established; the plant height estimation models of winter wheat breeding material were established respectively at jointing, booting, flowering and grain filling stages. Based on the ground measurement of winter wheat breeding material height (H), the accuracy of plant height of winter wheat breeding material extracted by DSM was verified.\u003c/p\u003e\u003cp\u003e【Result】 The results showed that the winter wheat coverage extracted based on the UAV images at the seedling stage had a high correlation with the measured plant density, and the R\u003csup\u003e2\u003c/sup\u003e was 0.8205.\u0026nbsp;The new winter wheat cultivar H extracted by DSM was significantly correlated with the measured H, and the fitted R\u003csup\u003e2 \u003c/sup\u003eand RMSE of the predicted plant height and the measured value were 0.9554 and 6.3233 cm, respectively. \u003c/p\u003e\u003cp\u003e【Conclusion】 The results indicated that the use of UAV aerial imagery to predict the plant density and plant height of winter wheat breeding material has good applicability, and can provide technical reference for future crop phenotype information monitoring.\u003c/p\u003e","manuscriptTitle":"Estimation of plant density and plant height of winter wheat breeding material based on UAV digital images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-14 15:14:19","doi":"10.21203/rs.3.rs-1469702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff85862f-f8b3-4a65-add0-4aa091bd69ed","owner":[],"postedDate":"April 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-05T01:32:09+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-14 15:14:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1469702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1469702","identity":"rs-1469702","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}