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Khakipour, Nazanin, Payandeh, Mahtab. (1402). Expectation of Chamomile Fundamental Oil Abdicate Matricaria chamomilla L. by Using the Artificial Neural Network System. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, (), -.
Nazanin Khakipour; Mahtab Payandeh. "Expectation of Chamomile Fundamental Oil Abdicate Matricaria chamomilla L. by Using the Artificial Neural Network System". نشریه علمی پژوهشی دانشگاه آزاد اسلامی, , , 1402, -.
Khakipour, Nazanin, Payandeh, Mahtab. (1402). 'Expectation of Chamomile Fundamental Oil Abdicate Matricaria chamomilla L. by Using the Artificial Neural Network System', نشریه علمی پژوهشی دانشگاه آزاد اسلامی, (), pp. -.
Khakipour, Nazanin, Payandeh, Mahtab. Expectation of Chamomile Fundamental Oil Abdicate Matricaria chamomilla L. by Using the Artificial Neural Network System. نشریه علمی پژوهشی دانشگاه آزاد اسلامی, 1402; (): -.

Expectation of Chamomile Fundamental Oil Abdicate Matricaria chamomilla L. by Using the Artificial Neural Network System

Journal of Ornamental Plants
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 14 بهمن 1402
نوع مقاله: Research Paper
نویسندگان
Nazanin Khakipour* ؛ Mahtab Payandeh
M.Sc. student of Horticultural Sciences, Science and Research Branch, Islamic Azad University,Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Artificial Neural Network (ANN)؛ calcium carbonate equivalent (CCE)؛ multilayer perceptron؛ nitrogen
آمار
تعداد مشاهده مقاله: 33


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