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

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نشریه آبیاری و زهکشی ایران
دوره 12، شماره 6 - شماره پیاپی 72
بهمن و اسفند 1397
صفحه 1396-1406

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