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پیش‌بینی تأثیر کود دامی و تردد تایر تراکتور بر تراکم خاک رسی با استفاده از شبکه های عصبی پرسپترون چندلایه | ||
| تحقیقات سامانه‌ها و مکانیزاسیون کشاورزی | ||
| مقاله 2، دوره 21، شماره 75، آذر 1399، صفحه 1-22 اصل مقاله (1.87 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/erams.2018.115790.1219 | ||
| نویسندگان | ||
| کامل قادرنژاد1؛ غلامحسین شاهقلی* 2؛ عارف مردانی کرانی3 | ||
| 1دانشجوی دکتریT گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی | ||
| 2دانشیار گروه مهندسی بیوسیستم، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران | ||
| 3دانشیار گروه مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه ارومیه، ارومیه، ایران | ||
| چکیده | ||
| یکی از راه‌های کاهش تراکم خاک، اضافه کردن کودهای آلی و مدیریت تردد در زمینهای کشاورزی است. در این تحقیق، کود دامی بر مبنای صفر، 45، 60 و 90 تن در هکتار با خاک رسی مخلوط شد. پس از گذشت 6 ماه (شهریور تا اسفندماه)، در چهار سطح تردد 1، 6، 11 و 16 و در سه سطح رطوبت 8، 11 و 14 درصد در سه عمق 10، 20 و 30 سانتی‌متری، تراکم خاک آزمایش شد. آزمایشها در انبارۀ خاک دانشگاه ارومیه زیر چرخ 220/65 R 21 تراکتور با استفاده از آزمونگر تک‌چرخ با بار ثابت 4 کیلونیوتن، فشار باد تایر برابر 110 کیلو پاسکال و سرعت پیشروی 88/2 کیلومتر بر ساعت، بررسی شد. در تیمار 90 تن کود دامی بر هکتار، نسبت به تیمار بدون کود، میزان کاهش جرم مخصوص و نشست خاک به ترتیب 7/14 و 94/6 درصد اندازهگیری شد. با افزایش تعداد تردد از 1 به 16 و افزایش رطوبت از 8 به 14 درصد، جرم مخصوص 21/7 و 92/7 درصد افزایش نشان یافته است. برای مدلسازی، از شبکۀ عصبی پرسپترون چند لایه (MLP-ANN)، با 6 نورون در لایۀ پنهان با تابع انتقال سیگموییدی و تابع انتقال خطی برای نورون خروجی، استفاده شد. مقایسۀ نتایج به دست آمده از مدل و نتایج تجربی نشان‌دهندۀ ضریب همبستگی 99/0 بین این مقادیر است. مقدار میانگین مربعات خطای مدل (MSE) و درصد میانگین مطلق خطای سیستم (MAPE) به ترتیب 0119071/0 و 0009641/0 به دست آمد که نشان از دقت بالای مدل شبکۀ عصبی در تخمین مقادیر تراکم خاک دارد. | ||
| کلیدواژه‌ها | ||
| آزمونگر تک چرخ؛ استوانه نمونه‌برداری؛ انباره خاک؛ مدلسازی | ||
| عنوان مقاله [English] | ||
| Predicting tthe Effect of Farmyard Manure and Number of Passes of Tractor Tire on Clay Soil Compaction Using Multilayer Perceptron Neural Networks | ||
| نویسندگان [English] | ||
| kamel قادرنژاد1؛ Gholamhossein Shahgholi2؛ Aref Mardani3 | ||
| 1Ph. D. Student, University of Mohaghegh Ardabili, Faculty of Agriculture and Natural Resources, Department of Biosystem Engineering, Ardabil, Iran | ||
| 2Associate Professor, University of Mohaghegh Ardabili, Faculty of Agriculture and Natural Resources, Department of Biosystem Engineering, Ardabil, Iran | ||
| 3Associate Professor, University of Urmia, Faculty of Agriculture, Department of Biosystem Engineering, Urmia, Iran | ||
| چکیده [English] | ||
| One way to reduce soil compaction is to add organic matter and to manage the field traffic. In this research, farmyard manure was incorporated into clay soil with rates of 0, 45, 60, 90 ton ha-1. After 6 months (September to March), at different numbers of tyre passes of 1, 6, 11 and 16, and three soil moisture contents of 8%, 11% and 14% soil compaction was evaluated measuring soil bulk density and soil sinkage. Experiments were conducted in the soil bin at the Urmia University under a single trector’s tire 220/65 R 21l under a constant load of 4 kN, inflation pressure of 110 kPa and at a forward velocity of 2.88 km hr-1. It was found that at manure rate of of 90 ton ha-1, comparing to no-manure treatment, soil bulk density and soil sinkage decreased by 14.7 and 6.94 percent, respectively. Also, increasing the number of tyre passes from 1 to 16 and increasing soil moisture content from 8 to 14 percent, increased soil bulk density 7.21% and 7.92%, respectively. For neural network modeling multilayer perceptron network with six neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was used. Comparison of neural network output and experimental results showed high correletion with correlation coefficient of R= 0.99 between them. The mean square error (MSE) of the model and mean absolute percentage error of the system (MAPE) were 0.0119071 and 0.0009641,respectivly, which showed high accuracy of neural network to model soil compaction. | ||
| کلیدواژه‌ها [English] | ||
| Cylindrical Cores, Modeling, Single Wheel Tester, Soil Bin | ||
| مراجع | ||
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