پیش بینی محصولات بنیادی روغن بابونه Abdicate Matricaria chamomilla L. با استفاده از سیستم شبکه عصبی مصنوعی
محورهای موضوعی : 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
کلید واژه: نیتروژن, شبکه عصبی مصنوعی (ANN), معادل کربنات کلسیم (CCE), پرسپترون چند لایه,
چکیده مقاله :
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.
The aim of this research was to forecast the proportion and production of chamomile essential oils by employing an artificial neural network system reliant on specific soil physicochemical characteristics. Various chamomile cultivation sites were explored, and 100 soil samples were transported to the greenhouse. The pH, EC, K, OM (organic matter), CCE (calcium carbonate equivalent), and clay content in the soils ranged from 8.75 to 7.94, 1.6 to 1.0, 381 to 135, 2.30 to 0.22, 69 to 16, and 55.6 to 32.0, respectively. Growth parameters, essential oil percentage, and yield were measured. The artificial neural network modeling aimed to predict essential oil concentration and yield using three sets of soil properties as predictors: Nitrogen (N), phosphorus (P), potassium (K), and clay; pH, EC, organic matter (OM), and clay; CCE, clay, silt, sand, N, P, K, OM, pH, and EC. Consequently, three pedotransfer functions (PTFs) were formulated using the multi-layer perceptron (MLP) with the Levenberg-Marquardt training algorithm to estimate chamomile essential oil content. The evaluation of results indicated that the third PTF (PTF3), developed using all independent variables, exhibited the highest accuracy and reliability. Furthermore, the findings suggested the feasibility of predicting chamomile essential oil concentration and yield based on soil physicochemical properties. This has significant implications for land suitability assessments, identifying areas conducive to chamomile cultivation, and planning for essential oil yields.
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