پیش بینی محصولات بنیادی روغن بابونه Abdicate Matricaria chamomilla L. با استفاده از سیستم شبکه عصبی مصنوعی
الموضوعات : مجله گیاهان زینتینازنین خاکی پور 1 , مهتاب پاینده 2
1 - دانشکده علوم خاکشناسی، واحد سوادکوه، دانشگاه آزاد اسلامی، سوادکوه، ایران
2 - دانشجوی کارشناسی ارشد علوم باغبانی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
الکلمات المفتاحية: نیتروژن, شبکه عصبی مصنوعی (ANN), معادل کربنات کلسیم (CCE), پرسپترون چند لایه,
ملخص المقالة :
هدف از این تحقیق، پیش‌بینی نسبت و تولید اسانس بابونه با استفاده از یک سیستم شبکه عصبی مصنوعی متکی بر ویژگی‌های فیزیکوشیمیایی خاص خاک بود. سایت های مختلف کشت بابونه مورد بررسی قرار گرفت و 100 نمونه خاک به گلخانه منتقل شد. pH، EC، K، OM (ماده آلی)، CCE (معادل کربنات کلسیم) و میزان رس در خاک ها از 8.75 تا 7.94، 1.6 تا 1.0، 381 تا 135، 2.30 تا 0.22، 69 تا 16، و 6.5 متغیر بود. به ترتیب به 32.0 رسید. پارامترهای رشد، درصد اسانس و عملکرد اندازه گیری شد. مدل‌سازی شبکه عصبی مصنوعی با هدف پیش‌بینی غلظت و عملکرد اسانس با استفاده از سه مجموعه از ویژگی‌های خاک به عنوان پیش‌بینی‌کننده انجام شد: نیتروژن (N)، فسفر (P)، پتاسیم (K)، و خاک رس. pH، EC، مواد آلی (OM) و خاک رس. CCE، خاک رس، سیلت، ماسه، N، P، K، OM، pH و EC. در نتیجه، سه تابع انتقال (PTF) با استفاده از پرسپترون چند لایه (MLP) با الگوریتم آموزشی Levenberg-Marquardt برای تخمین محتوای اسانس بابونه فرموله شد. ارزیابی نتایج نشان داد که PTF سوم (PTF3) که با استفاده از تمامی متغیرهای مستقل توسعه یافته است، بالاترین دقت و پایایی را از خود نشان می دهد. علاوه بر این، یافته‌ها امکان پیش‌بینی غلظت و عملکرد اسانس بابونه را بر اساس ویژگی‌های فیزیکوشیمیایی خاک پیشنهاد کرد. این پیامدهای قابل توجهی برای ارزیابی تناسب زمین، شناسایی مناطق مساعد برای کشت بابونه و برنامه ریزی برای بازده اسانس دارد.
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