An efficient in vitro plant regeneration system was established for elite, recalcitrant Malaysian indica rice, Oryza sativa L. CV. MR 219 using mature seeds as explant on Murashige and Skoog and Chu N6 media containing 2,4-dichlorophenoxy acetic acid and kinetin either alone or in different combinations. L-proline, casein hydrolysate and L-glutamine were added to callus induction media for enhancement of embryogenic callus induction. The highest frequency of friable callus induction (84%) was observed in N6 medium containing 2.5 mg l(-1) 2,4-dichlorophenoxy acetic acid, 0.2 mg l(-1) kinetin, 2.5 mg l(-1) L-proline, 300 mg l(-1) casein hydrolysate, 20 mg l(-1) L-glutamine and 30 g l(-1) sucrose under culture in continuous lighting conditions. The maximum regeneration frequency (71%) was observed, when 30-day-old N6 friable calli were cultured on MS medium supplemented with 3 mg l(-1) 6-benzyl aminopurine, 1 mg l(-1) naphthalene acetic acid, 2.5 mg l(-1) L-proline, 300 mg l(-1) casein hydrolysate and 3% maltose. Developed shoots were rooted in half strength MS medium supplemented with 2% sucrose and were successfully transplanted to soil with 95% survival. This protocol may be used for other recalcitrant indica rice genotypes and to transfer desirable genes in to Malaysian indica rice cultivar MR219 for crop improvement.
Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.