Improving postharvest quality and vase life of cut rose flowers by pre-harvest foliar co-applications of γ-aminobutyric acid and calcium chloride.

Rose flowers (Rosa hybrida L.) are highly perishable and have a limited vase life. This study evaluated the effects of preharvest foliar applications of γ-aminobutyric acid (GABA) and calcium chloride (CaCl2), individually and combined, on antioxidant responses and vase life of cut Jumilia rose flowers. Treatments included foliar sprays of GABA at 0, 20, 40, and 60 mM and CaCl2 at 0, 0.75%, and 1.5%, applied in a factorial design within a completely randomized setup before harvest. Results showed GABA and CaCl2 interaction (especially, 60 mM GABA and 1.5% CaCl2) significantly increased enzymatic antioxidants including superoxide dismutase, catalase, and peroxidase, as well as non-enzymatic antioxidants such as flavonoids, carotenoids, phenolics, and antioxidant activity in petals compared to control. SOD activity in roses, treated with CaCl2 (1.5%) and GABA (60 mM), peaked at 7.86 units. mg-1 protein min-1, showing a nearly 2.93-fold increase over the control (2.68 units. mg-1 protein min-1). A parallel trend was observed for CAT activity. These treatments also reduced petal malondialdehyde content and polyphenol oxidase activity. Protein content and vase life duration increased in all treatments. Plants treated with a combination of GABA (20 mM) and CaCl2 (0.75%), GABA (60 mM) and CaCl2 (1.5%), or GABA (40 mM) individually exhibited the longest vase life duration. The co-application of GABA and CaCl2 improved the antioxidant activity and postharvest quality of cut roses by reducing PPO activity and MDA contents, increasing protein content and prolonging vase life. This treatment is a potential postharvest strategy to improve antioxidant capacity and delay senescence in cut roses.

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models.

Festuca ovina L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6-4-8-1) which had RMSE, MAE and R2 scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.

Modeling callus induction and regeneration in an anther culture of tomato (Lycopersicon esculentum L.) using image processing and artificial neural network method.

Doubled haploids, subsequent to haploid induction, have wide range of applications in basic and applied plant studies. Various parameters can affect the efficiency of haploid induction through an anther culture of tomato. The hybrid system of image processing-artificial neural network (ANN) was used to better understand callus induction and regeneration in an anther culture of tomato. The effect of parameters such as plant genotype, the concentrations of 2,4-dichlorophenoxyacetic acid (2,4-D) and kinetin (Kin) plant growth regulators, the concentration of gum arabic (GA) additive, the cold pretreatment duration, and flower length on callus induction percentage and number of regenerated calli in an anther culture of tomato were studied using multiple linear regression (MLR) and ANN models. The precise flower bud length was measured using an image processing technique. The 4',6-diamidino-2-phenylindole (DAPI) analysis showed that the flowers with 5-6.9 mm length had the highest percentage of the mid- to late-uninucleate microspore stage. The best ANN model for both callus induction percentage and number of regenerated calli was a model with one hidden layer, 12-15 neurons in the first hidden layer, Levenberg-Marquardt learning algorithm, and Tan-Sigmoid transfer function in hidden layer, based on the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics. The scatter plot of measured values versus the predicted values showed the superiority of the ANN to MLR model to predict the callus induction percentage in an anther culture of tomato. The sensitivity analysis of MLR and ANN models revealed the plant genotype and 2,4-D concentration as the most important factors affecting both callus induction percentage and number of regenerated calli. Since tomato is a recalcitrant plant to androgenesis-based pathway of haploid induction, therefore the results of the present study can be helpful to develop an efficient haploid induction protocol in tomato through an anther culture pathway.