Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods

Abstract

Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m2), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R2 of EOY% = 0.929, R2 of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R2 of EOY% = 0.553, R2 of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m2, NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m2, NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs.

Effectiveness of Essential Oils from Three Medicinal Plants Against Bayoud Disease (Fusarium oxysporum f. sp. albedinis) of Date Palm (Phoenix dactylifera L.)

“…The rate obtained from Foeniculum vulgare samples was 2.31% greater than that obtained from Citrus sinensis sample which was 1.8%, followed by Artemisia herba alba samples 1.22% (Table 1). This rate is low when compared to that obtained from Foeniculum vulgare from Iran and Turkey, which is 3.77 and 2.67% respectively (Sabzi-Nojadeh et al, 2021). In addition, the essential oil yields for Citrus sinensis fresh fruits reported in this study are similar to those reported by Bhandari et al (2021).…”

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

“…A slightly higher accuracy of the ANN model (R = 0.99) when compared to SVM (R = 0.97) was reported by Afradi and Ebrahimabadi [47] who used AI methods to predict the penetration rate of tunnel boring machine. Sabzi-Nojadeh et al [48] compared the accuracy of ANN and MLR models used to predict the oil yield and trans-anethole yield of fennel populations; ANN performed better (R = 0.96 and R = 0.88) than MLR (R = 0.74 and R = 0.68).…”

Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times

“…Tese cells do not come together randomly, but rather follow a specifc architecture that consists of three types of neuron layers: input, hidden, and output layers. Te input layer is responsible for taking in information from the outside world and transferring it to the hidden layers [22][23][24]. Some networks do not process any information in the input layer at all.…”