پیش‌بینی عملکرد زعفران برمبنای خصوصیات خاک با استفاده از مدل‌های رگرسیونی و شبکه‌های عصبی مصنوعی در منطقه وامنان استان گلستان

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانش‌آموخته دکترا، گروه خاکشناسی، دانشکده کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

2 دانشیار، گروه خاکشناسی، دانشکده کشاورزی و صنایع غذایی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران

3 دانشیار، گروه خاکشناسی، دانشکده کشاورزی، دانشگاه تبریز، تبریز

10.22048/jsat.2020.240519.1404

چکیده

زعفران (Crocus sativus L.)یکی از محصولات کشاورزی با ارزش می­باشد که فقط در مناطق محدودی از دنیا کشت می­شود. امروزه با توجه به ارزش اقتصادی زعفران، کشاورزان زیادی بدون توجه به توانایی و قابلیت اراضی برای کشت این گیاه، صرفاً با توجه به مشابهت اقلیمی اقدام به کشت آن در برخی مناطق کشور نموده­اند که گاهی اوقات نتایج رضایت­بخشی در پی نداشته است. پیش­بینی عملکرد زعفران با توجه به خصوصیات خاک می­تواند به ارزیابی قابلیت اراضی برای کشت این گیاه ارزشمند کمک نماید. بدین منظور در یکی از مناطق جدید کشت زعفران در منطقه وامنان استان­ گلستان، تعداد 100 نمونه خاک برداشت و خصوصیات فیزیکی و شیمیائی شامل درصد اجزای تشکیل دهنده بخش معدنی بافت خاک، عناصر غذایی فسفر و پتاسیم قابل دسترس، نیتروژن کل، شاخص واکنش خاک، هدایت الکتریکی، ماده آلی و کربنات کلسیم معادل پس از برداشت، وزن تر گل زعفران بر حسب کیلوگرم در هکتار به دست آمد. با استفاده از شبکه­ عصبی مصنوعی و ایجاد مدل­های متفاوت با مجموعه داده­های متفاوتی از خصوصیات خاک به عنوان ورودی و عملکرد زعفران به عنوان خروجی، توانایی این مدل در پیش­بینی عملکرد زعفران با مدلهای رگرسیونی مقایسه شد. بر اساس نتایج ضریب همبستگی، مؤثرترین عوامل بر عملکرد زعفران، فسفر قابل دسترس و ماده آلی بودند. بررسی نتایج مدل­های ایجاد شده در دوره آزمون نشان داد مقادیر ضریب تبیین (R2) از 45/0 تا 89/0 متغیر می­باشد. با بررسی مدل­های برتر می­توان نتیجه­گیری نمود که مدل بهینه در برآورد عملکرد زعفران وقتی به دست آمد که فسفر، ماده آلی، آهک و پتاسیم ورودی­های مدل بودند و مقادیر R2 و ریشه میانگین مربعات خطا (RMSE) آن نیز به ترتیب برابر 874/0 و 996/0 کیلوگرم بر هکتار به دست آمدند.  

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Prediction of Saffron Yield based on Soil properties Using Regression and Artificial Neural Networks Models in the Vamenan Region of Golestan Province

نویسندگان [English]

  • Fatemeh Tashakkori 1
  • Ali Mohammadi Torkashvand 2
  • Abbas Ahmadi 3
  • Mehrdad Esfandiari 2
1 Ph.D, Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 Department of Soil Science, University of Tabriz, Tabriz, Iran
چکیده [English]

Saffron (Crocus sativus L.) is one of the most expensive crop which is grown in restricted areas of the world. Due to its economic values, some farmers, based on similarities of climatic conditions have cultivated it in some regions of country regardless of land capability and suitability, which sometimes the result was not satisfactory. Saffron yield prediction based on soil properties enables us to assess the land suitably for cultivation of this valuable plant. For this purpose, 100 soil samples were collected from Vamenan Saffron fields in Golestan province and the soil chemical and physical properties including the percentage of constituents of the mineral part of soil texture (Sand, Silt, Clay), Phosphorus, potassium, Nitrogen, pH, Electrical Conductivity (EC), Organic matter and Calcium Carbonate Equivalent were measured. In addition, the weight of Saffron wet flower (kg.Ha-1) was measured. In the present study, various combinations of soil properties as input were applied and nine models were developed using artificial neural networks and multiple linear regression models for predicting the saffron yield. Performance of the models was validated using Root Mean Square Error (RMSE), Correlation Coefficient (R) and Geometric Mean of Error Ratio (GMER) methods. The results of the correlation analyses showed phosphorus and organic matter are most effective factors in the production of Saffron. Results showed that performance of the models is much different where R2 value varies from 0.45 to 0.89. Comparing the performance of Saffron yield estimation models indicated the optimal model was obtained from the combination of phosphorous, organic matter, potassium and calcium carbonate equivalent as input and values of R2 and RMSE equal to 0.874 and 0.996 kg.ha-1, respectively.Evaluation of model results indicated that the coefficient varied was obtained from 0.45 to 0.89. The best model in saffron yield estimation was obtained when phosphorous, organic matter, potassium and electrical conductivity were as the input, so that values of R2 and root mean square error (RMSE) were obtained 0.891 and 0.89 kg.ha-1, respectively.

کلیدواژه‌ها [English]

  • Saffron Yield
  • Soil texture
  • Multilayer Perceptron
  • Phosphorus
  • Golestan
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