مدل‌های آماری برای پیش بینی غیر تخریبی سطح برگ پیاز (Allium cepa L.)

نوع مقاله : مقاله کامل علمی پژوهشی

نویسندگان

1 دانشجوی دکتری اگرواکولوژی، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

2 نویسنده مسئول، استاد گروه اگرواکولوژی، دانشکده کشاورزی، دانشگاه فردوسی مشهد‌، مشهد، ایران‌

3 استاد گروه اگرواکولوژی، دانشکده کشاورزی، دانشگاه فردوسی مشهد‌، مشهد، ایران‌

4 استادیار گروه علوم باغبانی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، ایران.

چکیده

مدل‌های آماری برای پیش‌بینی غیر تخریبی سطح برگ پیاز (Allium cepa L.)

سابقه و هدف: اهمیت اندازه‌گیری سریع، غیرمخرب و دقیق سطح برگ در مطالعات زراعی و فیزیولوژیکی به خوبی شناخته شده است و نیاز به برآوردهای سریع و غیرتخریبی سطح برگ منجر به مطالعاتی در رابطه با ابعاد و سطح برگ‌ها گردیده است. بعلاوه این مشکل در مورد گونه‌های که دارای برگ‌های با شکل نامتعارف بارزتر است. در حال حاضر اطلاعات محدودی در این مورد برای پیاز (Allium cepa L.) در دسترس است‌. هدف از این مطالعه ایجاد مدل رگرسیونی با تعین اعتبار آماری برای پیش‌بینی اندازه سطح برگ پیاز به صورت غیرتخریبی در جنوب ایران می‌باشد.



مواد و روش‌ها: به منظور تهیه نمونه‌های گیاهی مورد نیاز جهت اندازه گیری متغیرهای سطح برگ، آزمایشی در سال زراعی 1400-1399 در زمینی به مساحت ۳۰۰ متر مربع در استان هرمزگان-دهستان رهداد اجرا شد. تعداد 40 برگ از بوته‌های پیاز کشت شده در مزرعه به طور تصادفی انتخاب شدند، نیمی از آنها برای واسنجی (کالیبراسیون) و نیم دیگر برای تعیین اعتبار مدل های ریگرسیون مورد استفاده قرار گرفت و از روش استاندارد (LICOR LI-3000C) برای اندازه گیری سطح واقعی برگ‌ها استفاده شد. قبل از اندازه گیری سطح برگ، دو متغیر اصلی یعنی طول و عرض برای هر برگ به طور دقیق تعیین شد. برای انتخاب بهترین متغیرها و به‌دست آوردن معادله تخمین سطح برگ از رگرسیون ساده (خطی) و رگرسیون خطی چندگانه استفاده شد. در نهایت اعتبارسنجی و ارزیابی دقت مدل‌های ارائه شده بر اساس جذر میانگین مربعات خطا (RMSE) و ضریب تعیین (R2) صورت گرفت.



یافته‌ها: تمام مدل‌های خطی برای توصیف سطح برگ (A)، با طول (L)، با عرض (W) و نیز معادلات با دو متغیر طول و عرض برگ در معرض تحلیل رگرسیون قرار گرفت. نتایج نشان داد که طول برگ متغیر بهتری برای توصیف سطح برگ پیاز است و عرض برگ توصیف خوبی از سطح برگ نشان نمی‌دهد. بر اساس یافته‌های این تحقیق مدل پیش بینی سطح برگ با حضور هر دو متغیر طول و عرض:

A= 0.121 + 0.01537 L2 + 0.3225 L×W

با 64/8 RMSE% = و 91/0 R2 = بهترین برآورد را داشت.



نتیجه‌گیری: اندازه گیری سطح برگ به ویژه برای گیاهانی با برگ‌های لوله‌ای نظیر پیاز دشوار و وقت گیر است، چنین مدل‌های ریاضی ابزار بسیار مفیدی برای پیش بینی سطح برگ در بسیاری از گیاهان بدون استفاده از دستگاه‌های گران قیمت خواهد بود. مدل‌های این مطالعه روش ساده، سریع، غیر تخریبی و کم هزینه‌ای برای تخمین سطح برگ پیاز ارائه می‌کند که می‌تواند مورد استفاده پژوهشگران قرار گیرد.



کلید واژه: رگرسیون، سطح برگ، ضریب تبیین، طول برگ، عرض برگ



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کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Statistical models for non-destructive prediction of common onion (Allium cepa L.) leaf area

نویسندگان [English]

  • Younes Rameshjan 1
  • Alireza Koocheki 2
  • Mehdi Nassiri Mahallati 3
  • Leila Jafari 4
1 Ph.D. Student of Agroecology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
2 Corresponding Author, Professor, Dept. of Agroecology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
3 Professor, Dept. of Agroecology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
4 Assistant Prof., Dept. of Horticultural Science, Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran
چکیده [English]

Statistical models for non-destructive prediction of onion (Allium cepa L.) leaf area

Background and objectives: The importance of rapid, non-destructive, and accurate estimation of leaf area (LA) in agronomic and physiological studies is well known. The need for such estimates of leaf area in particular for leaves with unusual shapes has led to studies relating leaf dimensions and leaf area. However, a search of literature revealed that little information is available for short day onion (Allium cepa L.). The objective of this study was to develop a statistically validated regression model for leaf area prediction from simple non-destructive measurements for onion cultivated in the South of Iran.

Materials and methods: In order to prepare the required plant samples to measure leaf area parameters, an experiment was conducted in the cropping year 2020-2021 in a field of 300 square meters located in Hormozgan province-Rahdad district. The short-day cultivar Takii was used because of its wide planting area in the province. 40 leaves were chosen at random from plants growing in the farm, half were left for validation of the model and the other 20 leaves were subjected to measurement and calibration. The standard method (LICOR LI-3000C) was used for measuring the actual areas of the leaves. Before measuring the leaf area, two main parameters related to leaf length and width were accurately determined for each leaf. To select the best variables and the regression model for estimating leaf area, the fitted simple and multiple linear regression were subjected to the stepwise elimination method. Finally, the accuracy of the selected models was validated using the root mean square error (RMSE) and coefficient of determination (R2).

Results:

All linear models for estimation of leaf area with width (W) and length (L) as well as models including both variables were subjected to regression analysis. The results indicated that leaf length (L) is more appropriate variable for estimating onion leaf area and leaf width (W) was not able to properly describe leaf area. Based on the findings of this research leaf area estimation model with both W and L had the best prediction, with RMSE% = 8.64, RMSE= 1.76 and R2= 0.91. (Area= 0.121 + 0.01537 L2 + 0.3225 L×W).

Conclusion: As the understanding of plant growth and development has been increasing, such mathematical models will be very useful tools for the prediction of leaf area for many plants without the use of expensive devices. Thus, the models from the present study will enable researchers of plant growth modeling to predict leaf area non-destructively with the equations developed.

Keywords: Leaf Area, Leaf Length, Leaf Width, Regression, Coefficient of determination.

کلیدواژه‌ها [English]

  • Leaf Area
  • Leaf Length
  • Leaf Width
  • Regression
  • coefficient of determination

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پژوهش‌های تولید گیاهی
دوره 31، شماره 1
فروردین 1403
صفحه 127-147

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