Browsing by Subject "Artifcial intelligence"

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Open Access
Artificial neural network and decision tree facilitated prediction and validation of cytokinin‑auxin induced in vitro organogenesis of sorghum (Sorghum bicolor L.)
(Springer Dordrecht, 2023-04-05) Aasim, M.; Ali, Seyid Amjad; Altaf, M. T.; Ali, A.; Nadeem, M. A.; Baloch, F. S.
In this study, in vitro regeneration protocol of sorghum (Sorghum bicolor) was successfully established by using direct organogenesis from a mature zygotic embryo explant. The used basal medium encompassed Murashige and Skoog medium (MS) supplemented with 2–4 mg/L Benzylaminopurine (BAP) alone or with 0.25 mg/L Indole butyric acid (IBA) or Naphthalene acetic acid (NAA). Results demonstrated a significant impact of cytokinin-auxin on shoot count (1.24–3.46) and shoot length (2.80–3.47 cm). Maximum shoot count (3.46) and shoot length (3.97 cm) were achieved on the MS medium enriched with 2 mg/L BAP + 0.25 mg/L NAA and 2.0 mg/L BAP, respectively. To ascertain the impact of BAP alone, BAP + IBA, and BAP + NAA, the data were also analyzed by using a factorial regression model. Pareto chart and normal plots were used to check either the positive or negative impact of input variables on output variables. To further explore the association between BAP + IBA and BAP + NAA on shoot count and shoot length, contour and surface plots were also built. Three different artificial intelligence-based models along with four different performance metrics were utilized to validate the predicted results. Multilayer perceptron (MLP) model performed more efficiently (R2 = 0.799 for shoot count and R2 = 0.831 for shoot length) as compared to the decision tree-based algorithms of random forest (RF) – (R2 = 0.779 for shoot count and R2 = 0.786 for shoot length) and extreme gradient boost (XGBoost) – (R2 = 0.768 for shoot count and R2 = 0.781 for shoot length). As plant tissue culture protocol is a powerful tool for genetic engineering and genome editing of crops, integration of different artificial intelligence-based models can lead to improvement of sorghum with the aid of biotechnological tools.
Open Access
Synergizing LED technology and hydropriming for intelligent modeling and mathematical expressions to optimize chickpea germination and growth indices
(Springer New York LLC, 2024-03-29) Aasim, Muhammad; Akın, Fatma; Ali, Seyid Amjad
The influence of hydropriming and Light Emitting Diodes (LED) on germination and growth indices, followed by optimizing and validation via artificial intelligence-based models was carried out in this research. White LEDs (W-LEDs) were more effective by yielding the most effective growth indices, such as mean germination time (MGT) (1.11 day), coefficient of variation of germination time (CV t ) (20.72%), mean germination rate (MR) (0.81 day-1), uncertainty (U) (0.40 bit), and synchronization (Z values) (0.79); the optimum MGT (1.09 day), CV t (15.97%), MR (0.77 day-1), U (0.32 bit), and Z (0.55) values were found after 2 h of hydropriming, which was responsible for all efficient growth indicators. W-LEDs with 1 h hydropriming proved to be the ideal LED and hydropriming combination. Results on growth indices for in vitro seedlings were completely different from those on germination indices, and the most desirable germination indices were linked to red LEDs (R-LEDs). Whereas 4 h hydropriming was most effective for the post-germination process. Pareto charts, normal plots, contour plots, and surface plots were created to optimize the input variables. Finally, the data were predicted using Arificial Neural Network (ANN) inspired multilayer perceptron (MLP) and machine learning-based random forest (RF) algorithms. For both models, plant height was correlated with maximum R 2 values. Whereas, all output variables had relatively low mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) scores, indicating that both models performed well. The results of this investigation disclosed a link between certain LEDs and hydropriming treatment for in vitro germination indices and plant growth.