عملکرد مدل‌های شبکه‌ عصبی پرسپترون چندلایه و توابع با پایه شعاعی در برآورد میزان محصول نیشکر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 مکانیزاسیون کشاورزی، گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران

2 گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران

چکیده

اهداف: با توجه به اهمیت بالای تولید پایدار محصولات کشاورزی در واحد‌های کشت و صنعت نیشکر، باید از سیستم‌های هوشمند مانند شبکه‌های عصبی مصنوعی جهت مدیریت واحد‌های مزرعه استفاده کرد. بدین منظور، هدف اصلی تحقیق، مقایسه عملکرد مدل‌های شبکه عصبی پرسپترون چندلایه و توابع پایه شعاعی به‌منظور مدل‌سازی و پیش‌بینی عملکرد نیشکر و بررسی عوامل موثر بر آن بود.
 
مواد و روش‌ها: این تحقیق از نوع تحلیلی بوده و پایگاه داده‌ها‌ی آن ماتریسی به ابعاد درایه بود. داده‌های مورد نیاز این تحقیق طی سال‌های زراعی 1395 تا 1398 از واحد کشت و صنعت نیشکر دعبل خزاعی به‌دست آمد. متغیرهای ورودی مدل و واحدهای آنان به‌ترتیب شامل میزان هدایت الکتریکی خاک (دسی‌زیمنس بر متر)، مقدار کود شیمیایی فسفات و نیتروژن (کیلوگرم بر هکتار)، مقدار آب مصرفی (مترمکعب بر هکتار)، همچنین، تعداد دفعات آبیاری، ماه برداشت محصول،  سن گیاه، واریته گیاه، و بافت خاک (بدون ابعاد) بودند. متغیر خروجی، میزان عملکرد (تن بر هکتار) بود. تجزیه و تحلیل توسط نرم‌افزار متلب 2017 انجام شد.
 
یافته‌ها: با مقایسه پارامترهای خطای میانگین درصد خطای مطلق و جذر میانگین مربعات خطا و با توجه به شاخص‌های ضریب تبیین و بازده مدل، مدل توابع پایه شعاعی به‌ترتیب با داشتن 064494/0(درصد)، 037686/0، 7576/0 و 800409/0(بدون ابعاد) در مرحله اعتبارسنجی به عنوان مدل برتر انتخاب شد. همچنین، مدل توابع پایه شعاعی، متغیرهای واریته گیاه و میزان هدایت الکتریکی خاک را مهم‌ترین عامل موثر بر میزان عملکرد محصول نیشکر بیان کرد.
 
نتیجه‌گیری: با انتخاب واریته مناسب گیاه نیشکر و کنترل میزان هدایت الکتریکی خاک می‌توان عملکرد در واحد سطح را افزایش داد و سبب بهره‌وری بیشتر از نهاده‌ها و تولید پایدارتری شد.
 

کلیدواژه‌ها


عنوان مقاله [English]

Performance of Multilayer Perceptron Neural Network Models and Radial-Based Functions in Estimation of Sugar-cane Crop Yield

نویسندگان [English]

  • Sina Sharifi 1
  • Nasim Monjezi 2
  • Negar Hafezi 1
چکیده [English]

Background and objective: According to the high importance of sustainable crop production in the agro-industry units, intelligent systems such as artificial neural networks should be used to manage farm units.Therefore, the main purpose of this study was to compare the performance of MLP (Multi-Layer Perceptron) and RBF (Radial Basis Functions) neural network models in order to modeling and estimating of the sugarcane crop yield and investigate the factors affecting it.
 
Materials and Methods: The study was analytical and its database contained of a matrix  elements. Required data for this research were obtained from the Debel Khazaei sugar cane agro-industry farm during the years 2016 to 2019. The input variables and their units were soil electrical conductivity (dS.m-1), Phosphate and Nitrogen chemical fertilizer (kg.ha-1), water consumption (m3.ha-1), also, irrigation times, month of harvest, age of crop, sugarcane variety, soil texture (non-dimensional), respectively. The analysis was performed by MATLAB 2017 software.
 
Results: By comparing the error parameters of RMSE (Root Mean Square Error) and the MAPE (Mean Absolute Percentage Error), and according to indexes of R2 (coefficient of determination) and the EF (Model Efficiency) and, in the validation phase the RBF model was the best model with 0.064494 (%), 0.037686, 0.7576 and 0.800409 (non-dimensional) respectively. Also, the RBF model indicated that the sugarcane variety and soil electrical conductivity were the most important factors affecting the sugar-cane yield.
 
Conclusion: By selecting the appropriate variety of sugarcane and controlling the amount of electrical conductivity of the soil, the yield per unit area can be increased, resulting in greater productivity of the inputs and more sustainable production.
 

کلیدواژه‌ها [English]

  • Modeling
  • Network
  • Radial Basis Functions
  • Sugar-cane
  • Yield
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