پیش‌بینی دمای هوای یک گلخانه‌ با پوشش پلی اتیلن با استفاده از شبکه‌های عصبی مصنوعی، مطالعه موردی: منطقه جیرفت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران

2 گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه فردوسی مشهد، مشهد، ایران.

3 گروه مهندسی ماشینهای کشاورزی و مکانیزاسیون، دانشگاه علوم کشاورزی و منابع طبیعی خوزستان-ملاثانی، خوزستان، ایران.

چکیده

دمـا و کنترل آن در گلخانه یکی از پارامترهای مهم در گلخانه­ها بوده و نقش اساسی در اقتصادی بودن تولید دارد. با وجود این که گلخانه یک محیط بسته است ولی کاملاً از محـیط بیرون ایزوله نیست. بنابراین شرایط داخل گلخانه تحت تأثیر تغییـرات آب و هوایی بیرون دائماً تمایل به تغییر دارد. هدف از اجرای این تحقیق، تخمین دمای هوا در یک گلخانه با پوشش پلی اتیلن با توجه به پارامتر­های خارجی گلخانه شامل دمای هوا (Tout)، رطوبت نسبی هوا (Hout)، شدت تابش خورشید (S) و سرعت باد (V) با استفاده از روش­های مختلف شبکه­های عصبی مصنوعی شامل پرسپترون چند لایه (MLP)، تابع شعاع مدار (RBF) و عصبی-فازی (ANFIS) می­باشد. مقایسه بین مدل­های مختلف شبکه­های عصبی نشان داد که روش RBF با ضریب تبیین بالاتر )93/0 (R2=و خطای کمتر (25/2(RMSE= نسبت به دو روش MLP و ANFIS دارای عملکرد بهتر در پیش­بینی بود. نتایج ارزیابی مدل RBF برای پیش­بینی دما در ساعات آینده بیانگر خطای قابل قبول در پیش­ بینی توسط این مدل تا دو ساعت آینده بود و بنابراین کشاورزان زمان کافی برای فراهم نمودن تمهیدات لازم جهت جلوگیری از افزایش دما در گلخانه در ساعات آینده و صرفه جویی در مصرف انرژی خواهند داشت.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of Temperature in a Greenhouse Covered with Polyethylene Plastic Using Artificial Neural Networks, Case Study: Jiroft Region

نویسندگان [English]

  • Elham Bolandnazar 1
  • hassan sadrnia 2
  • Abbas Rohani 2
  • Morteza Taki 3
1 PhD student of Biosystems Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
2 Associate Professor of Biosystems Engineering Department, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
3 Department of agricultural machinery and mechanization, Agricultural Sciences and Natural Resources University of Khuzestan-Mollasani, Khuzestan, Iran.
چکیده [English]

Internal temperatures of greenhouse and its control is one of the important parameters in greenhouses and plays a key role in the economics of production. Although the greenhouse is a closed environment, it is not completely isolated from the outside. Therefore, the conditions inside the greenhouse are constantly changing under the influence of outside climate change. The purpose of this study was to estimate the internal air temperature of polyethylene greenhouse with respect to the external parameters of the greenhouse including air temperature (Tout), air relative humidity (Hout), solar radiation (S) and wind speed (V). For this purpose, different method of artificial neural networks including Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Adaptive Nero Fuzzy Inference System (ANFIS) were used. Comparison between different neural network models showed that RBF method had better prediction performance than MLP and ANFIS with higher coefficient of determination (R2=0.93) and lower error (RMSE=2.25). The results of the RBF model estimation for the prediction future temperature indicated an acceptable error in the prediction by the model for the next two hours and thus, the farmers had enough time to provide the necessary measures to prevent the greenhouse temperature rise in the future and save in energy consumption.

کلیدواژه‌ها [English]

  • "Greenhouse temperature"
  • " Polyethylene Cover"
  • "artificial neural network"
  • " modeling"

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مهندسی بیوسیستم ایران
دوره 51، شماره 1
فروردین 1399
صفحه 125-137

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سابقه مقاله

  • تاریخ دریافت: 14 آبان 1398
  • تاریخ بازنگری: 19 آذر 1398
  • تاریخ پذیرش: 01 دی 1398

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