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Evaluating the optimization of irradiation components of mung bean seeds with ultrasound for increased seedling vigor, using artificial neural network
Shamsaldin Skandarnejad, Manoochehr Gholipoor, Hassan Makarian,
Volume 8, Issue 2 ((Autumn & Winter) 2022)
Abstract

Extended Abstract 
 Introduction: A large number of experimental evidence indicates the positive effect of irradiating the seed with ultrasonic waves; so that irradiation causes the production of a more vigorous seedling. Conversely, inappropriate intensity and duration of irradiation can impose deleterious effects on seedlings by damaging the enzymatic activity. There are complex inter-and intra-relations between irradiation components (pre-soaking duration, temperature, and duration of irradiation) and response variables [seedling dry weight (SDW) and percent of abnormal seedlings (PAS)]. Therefore the balance values of the irradiation components cannot be precisely obtained by mean comparison. This study aimed to optimize (finding the balance values of) irradiation components for increased SDW, but diminished PAS of mung bean, using an artificial neural network.
 Materials and Methods: A factorial experiment was conducted based on a completely randomized design with three replications. The factors were six pre-soaking durations (2, 4, 6, 8, 10, and 12 hours), 5 irradiation durations (0, 3, 6, 9 and 12 minutes), and 4 irradiation temperatures (17, 22, 27, and 32 oC). The 25 seeds were chosen for each petri dish. The multi-layer perceptron neural network was used to quantify the relations between variables; the experimental factors were used as the input (regressors), and PAS and SDW as the output of the model (response variables).
Results: The analysis of variance results indicated that the simple and interactive effects of factors were significant on PAS and SDW. The structure 3:3:2 of the neural network, which is based on Secant Hyperbolic function, was suitable. The SDW and PAS were negligibly different for the contribution of the factors in determining their changes. In terms of relative contribution, the factors ranked from higher to lower as irradiation duration, irradiation temperature, and pre-soaking duration. The optimized values of components of irradiation by the neural network were irradiation temperature of 17.96 oC, irradiation duration of 5.3 minutes, and pre-soaking duration of 11.25 hours. For these components, SDW was 27% higher, and PAS tended to be 0.6% lower, compared to the best component combination gotten by mean comparison.
Conclusion: Due to the highly strong interaction of irradiation components on seedling growth, the effect of component (s) tends to be changed intensively with changing the quantity of each component. In terms of finding the best combination of irradiation components, the neural network was more efficient than the mean comparison. Therefore, the neural network could be used as a complementary procedure in such investigations.

Highlights:
1- Irradiation components including irradiation duration and temperature, and pre-soaking duration affected seedling growth.
2- Inappropriate irradiation components diminished seedling growth to the below of no-irradiation conditions.
3- The optimum (balanced) levels of irradiation components increased seedling growth remarkably.


Quantifying the Germination of Fagopyrum esculentum Moenc. Using Regression and Thermal-Time Models
Meysam Miri, Mohammdreza Amerian, Mohsen Edalat, Mehdi Baradaran Firouzabadi, Hasan Makarian,
Volume 8, Issue 2 ((Autumn & Winter) 2022)
Abstract
Extended Abstract
 Introduction: Germination is considered the first and most important stage of establishment and consequently, successful competition which is influenced by genetic and environmental factors. Among the environmental factors influencing the germination, temperature and light are the most important ones. Using different models, the germination response of seeds to temperature can be quantified; therefore, this study was performed to investigate the effect of temperature on germination and to quantify the germination response of Buckwheat seed (Fagopyrum esculentum Moenc) to temperature using nonlinear regression models and thermal-time model.
Materials and methods: The seeds were germinated in 4 replications of 25 seeds under 8 constant temperature treatments (5, 10, 15, 20, 25, 30, 35 and 40 ° C). Using a three-parameter logistic model, Buckwheat seed germination was quantified at different temperature levels and the percentage and time to reach 50% germination were obtained. Four nonlinear regression models and a thermal-time model were used to quantify the response of Buckwheat seed germination rate to temperature. To compare the models and determine the most appropriate model, the root mean square error index (RMSE), coefficient of determination (R2), coefficient of variation (CV) and standard error (SE) were used for the observed germination rate versus the predicted germination rate.
Results: The results indicated that temperature affected the seedling length, normal seedling percentage, seed vigor and the germination rate as well as germination percentage. Also, the results showed that germination characteristics increased with increasing temperature up to 20 and 25 °C. Comparison of the three models based on the root mean square error (RMSE) of germination time, the coefficient of determination (R2), CV and SE, the best model to determine the cardinal temperatures of Fagopyrum esculentum was the dent-like model. The results of thermal-time model showed that the base temperature of Fagopyrum esculentum seeds was 4.01 ° C and the thermal-time coefficient was 1242.6 h° C.
Conclusion: Utilization of non-linear regression models (segmented, dent-like and beta) and thermal-time model to quantify the germination response of Fagopyrum esculentum response to different temperatures led to acceptable results. Therefore, germination rate and percentage may be predicted using the outputs of these models at different temperatures.

Highlights:
  1. The best temperature for Fagopyrum esculentum Moenc. seed germination is 20-25 Celsius.
  2. The dent-like model was determined the most appropriate model for estimating the cardinal temperatures of Buckwheat.

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