برآورد شاخص سطح برگ ذرت با استفاده دوربین دیجیتال اصلاح شده

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه آبیاری و زهکشی، دانشگاه تربیت مدرس، تهران، ایران

2 دانشیار گروه آبیاری و زهکشی دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار پژوهش، موسسه تحقیقات فنی و مهندسی کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، کرج، ایران

چکیده

شاخص سطح برگ (LAI) یکی از مهمترین شاخص‌های رشد گیاه و عملکرد محصول می‌باشد. بنابراین پایش توزیع مکانی و زمانی این شاخص در مزارع کشاورزی، می‌تواند بیانگر چگونگی کاربرد استراتژی‌های مدیریت مزرعه از جمله برنامه ریزی آبیاری و یکنواختی توزیع آب باشد. هدف این مطالعه، تشریح چگونگی کاربرد دوربین دیجیتال اصلاح شده به منظور تعیین دقیق توزیع مکانی شاخص سطح برگ در مزرعه ذرت علوفه‌ای است. تاکید این مقاله بر ترکیب دوربین تصویربرداری دیجیتال با یک فیلتر مخصوص (طراحی شده برای توسعه تصویربرداری چند طیفی در ناحیه مرئی (Vis) و مادون قرمز نزدیک (NIR)) است. به منظور حذف خطاهای موجود در تصاویر، اصلاحات دقیقی بر روی خطای سایه روشن تصویر، انحرافات هندسی، و اثرات بازتاب نقطه داغ، انجام شد. با توجه به رزولوشن مکانی بالا این سامانه تصویر برداری (در حد سانتیمتر) تفکیک پیکسل‌های دارای پوشش گیاهی از پیکسل‌های بدون پوشش گیاهی در تصاویر استخراج شده، به خوبی انجام شد و این موضوع در تعیین درصد پوشش گیاهی (تراکم رشد محصول) موثر بود. شاخص سطح برگ بیشترین همبستگی را با درصد پوشش گیاهی (919/0R2 = ) و بعد از آن با باند طیفی مادون قرمز نزدیک (741/0 R2 =) داشت. همبستگی بالایی بین دو باند طیفی قرمز و سبز با باند طیفی مادون قرمز نزدیک وجود داشت. این همبستگی باعث شد، اثر این دو باند طیفی در کنار باند طیفی مادون قرمز نزدیک، در مدلسازی شاخص سطح برگ معنادار نشود. در نتیجه مدلسازی شاخص سطح برگ تنها با دو پارامتر درصد پوشش گیاهی و باند طیفی مادون قرمز نزدیک انجام شد. ضریب تعیین تعدیل شده در مدل 966/0 بدست آمد که نشان می دهد 6/96 % از تغییرات شاخص سطح برگ در سطح مزرعه، توسط دو متغیر درصد پوشش گیاهی و باند طیفی مادون قرمز نزدیک وارد شده به مدل تبیین می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Estimating Leaf Area Index of a corn silage field Using a Modified Commercial Digital Camera

نویسندگان [English]

  • Mostafa Gooyandeh 1
  • seyed majid mirlatifi 2
  • Mehdi Akbari 3
1 Ph.D student Of Irrigation and Drainage, Department of Irrigation and Drainage Engineering, Tarbiat Modares University, Tehran, Iran
2 Associate Professor, Dept. of Irrigation and Drainage Engineering, Tarbiat Modares University, Tehran, Iran
3 Associate Professor, Department of Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
چکیده [English]

Leaf area index (LAI) is an important indicator of plant growth and yield. Therefore, monitoring the spatial and temporal distribution of LAI at agricultural farms could be a significant predictor of how well the various elements of farm management strategies such as irrigation scheduling and uniformity have been implemented. The purpose of this study is to outline how pictures taken by a modified digital camera can be used for estimating the LAI of a corn silage field. It focuses on how to utilize a combination of a simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains, to estimate LAI. In order to remove the sources of errors in the resulting images, procedures to perform image vignetting corrections, geometric distortions corrections, and elimination of radiometric bidirectional effects are suggested. Due to high spatial resolution of this imaging system (at the level of a few centimeters), separation of surfaces with and without plant cover was accomplished well. This separation process was also useful in determination of percentage of vegetation cover (crop density). The leaf area index had the highest correlation with the vegetation cover percentage (R2 = 0/919), and the NIR spectral band (R2 = 0/741). There was a high correlation between the two spectra of red and green with the NIR spectral band. This correlation indicates that with the presence of the NIR spectral band, the effect of red and green spectral bands on estimating leaf area index is insignificant. Therefore, a multivariable regression model was generated to estimate leaf area index as a function of only two parameters, namely vegetation cover percentage and spectral band NIR. The performance of the developed model was evaluated by comparing its predicted values of LAI with corresponding measured values. The adjusted coefficient of determination of this comparison was 96.6%, which indicates that 96.6% of the variation in the estimated leaf area index values is explained by the two variables (vegetation cover percentage and NIR spectral band) incorporated into the model.

کلیدواژه‌ها [English]

  • Unmanned Aerial Vehicle
  • Digital Image Processing
  • Percentage of Vegetation Cover
  • Remote Sensing

Ballesteros,R., Ortega,J., Hernández,D and Moreno,M. 2014. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing. Precision agriculture. 15.6: 579-592.

Baret,F., Hagolle,O., Geiger,B., Bicheron,P., Miras,B., Huc,M., Samain,O. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote sensing of Environment.110.3: 275-286.

Bendig,J., Bolten,A., Bennertz,S., Broscheit,J., Eichfuss, S and Bareth,G. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing. 6.11:10395-10412.

Boerma,H.R and Specht,J.E. 2004. Soybeans: improvement, production and uses. 3rd edition. USA. American Society of Agronomy.P1144.

Breda,N. J. 2003. Ground‐based measurements of leaf area index: a review of methods, instruments and current controversies. Journal of experimental botany. 54.392:2403-2417.

Bsaibes,A., Courault,D., Baret,F., Weiss,M., Olioso,A., Jacob,F., Desfond,V.  2009. Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring. Remote sensing of Environment. 113.4: 716-729.

Burkey,K.O and Wells,R. 1991. Response of soybean photosynthesis and chloroplast membrane function to canopy development and mutual shading. Plant physiology. 97.1: 245-252.

Campos-Taberner,M., García-Haro,F.J., Camps-Valls,G., Grau-Muedra,G., Nutini,F., Crema,A and Boschetti,M. 2016. Multitemporal and multiresolution leaf area index retrieval for operational local rice crop monitoring. Remote sensing of Environment. 187: 102-118.

Causi,G.L and De Luca,M. 2005. Optimal subtraction of OH airglow emission: A tool for infrared fiber spectroscopy. New Astronomy. 11.2: 81-89.

Darvishzadeh,R., Skidmore,A., Schlerf,M., Atzberger,C., Corsi,F and Cho,M. 2008. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. .Journal of photogrammetry and remote sensing. 63.4: 409-426.

Dente,L., Satalino,G., Mattia,F and Rinaldi,M. 2008. Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote sensing of Environment. 112.4: 1395-1407.

Eitel,J., Long,D., Gessler,P and Smith,A. 2007. Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status. International Journal of Remote Sensing. 28.18: 4183-4190.

Fang,H., Liang,S., Hoogenboom,G., Teasdale,J and Cavigelli,M. 2008. Corn‐yield estimation through assimilation of remotely sensed data into the CSM‐CERES‐Maize model. International Journal of Remote Sensing. 29.10: 3011-3032.

Gitin,A. 1993. Integral description of physical vignetting. Soviet Journal of Optical Technology. 60.8: 556-558.

Gómez-Candón,D., De Castro,A and López-Granados, F. 2014. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision agriculture. 15.1: 44-56.

Hansen,P and Schjoerring,J. 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote sensing of Environment. 86.4: 542-553.

Hassan-Esfahani,L., Torres-Rua,A., Jensen,A and McKee,M. 2015. Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sensing. 7.3: 2627-2646.

Jensen,T., Apan,A., Young,F and Zeller,L. 2007. Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform. Computers and Electronics in Agriculture. 59.1-2: 66-77.

Jonckheere,I., Fleck,S., Nackaerts,K., Muys,B., Coppin,P., Weiss,M and Baret,F. 2004. Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and forest meteorology. 121.1-2: 19-35.

Ke,L., ZHOU,Q.-b., WU,W.-b., Tian,X and TANG,H.-j. 2016. Estimating the crop leaf area index using hyperspectral remote sensing. Journal of integrative agriculture. 15.2:475-491.

Knyazikhin,Y., Martonchik,J., Myneni,R.B., Diner,D and Running,S.W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research: Atmospheres. 103.D24: 32257-32275.

Lebourgeois,V., Bégué,A., Labbé,S., Mallavan,B., Prévot,L and Roux,B. 2008. Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors. 8.11: 7300-7322.

Lelong,C.C., Burger,P., Jubelin,G., Roux,B., Labbé,S and Baret,F. 2008. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors. 8.5: 3557-3585.

Lorenzen,B and Jensen,A. 1988. Reflectance of blue, green, red and near infrared radiation from wetland vegetation used in a model discriminating live and dead above ground biomass. New Phytologist. 108.3: 345-355.

Lugg,D and Sinclair,T. 1981. Seasonal changes in photosynthesis of field-grown soybean leaflets. 2. Relation to nitrogen content. Photosynthetica.

Maki,M and Homma,K. 2014. Empirical regression models for estimating multiyear leaf area index of rice from several vegetation indices at the field scale. Remote Sensing. 6.6: 4764-4779.

Nebiker,S., Annen,A., Scherrer,M and Oesch,D. 2008. A light-weight multispectral sensor for micro UAV—Opportunities for very high resolution airborne remote sensing. The international archives of the photogrammetry, remote sensing and spatial information sciences. 37.B1: 1193-1199.

Pinter Jr,P.J., Hatfield,J.L., Schepers,J.S., Barnes,E.M., Moran,M.S., Daughtry,C.S and Upchurch,D. R. 2003. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing. 69.6: 647-664.

Rabatel,G., Gorretta,N and Labbe,S. 2014. Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: Theoretical and practical study. Biosystems Engineering. 117: 2-14.

Saberioon,M., Amin,M., Anuar,A., Gholizadeh,A., Wayayok,A and Khairunniza-Bejo,S. 2014. Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation. 32: 35-45.

Shou,L., Jia,L., Cui,Z., Chen,X and Zhang,F. 2007. Using high-resolution satellite imaging to evaluate nitrogen status of winter wheat. Journal of plant nutrition. 30.10: 1669-1680.

Thomson,S., Zimba,P., Bryson,C and Alarcon-Calderon,V. 2005. Potential for remote sensing from agricultural aircraft using digital video. Applied Engineering in Agriculture. 21.3: 531-537.

Williams,J., Kitchen,N., Scharf,P and Stevens,W. 2010. Within-field nitrogen response in corn related to aerial photograph color. Precision agriculture. 11.3: 291-305.

Xiao,X., He,L., Salas,W., Li,C., Moore Iii,B., Zhao,R., Boles,S. 2002. Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields. International Journal of Remote Sensing. 23.18: 3595-3604.

Yang,C., Westbrook,J. K., Suh,C.P.-C., Martin,D.E., Hoffmann,W.C., Lan,Y., Goolsby,J. A. 2014. An airborne multispectral imaging system based on two consumer-grade cameras for agricultural remote sensing. Remote Sensing. 6.6: 5257-5278.

Zhang,J.-H., Ke,W., Bailey,J and Ren-Chao,W. 2006. Predicting nitrogen status of rice using multispectral data at canopy scale1. Pedosphere. 16.1: 108-117.