Oil yield prediction for sunflower hybrid selection using different machine learning algorithms.

Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

Increase of isoflavones in the aglycone form in soybeans by targeted crossings of cultivated breeding material.

Isoflavones are a group of phytoestrogens, naturally-occurring substances important for their role in human health. Legumes, particularly soybeans (Glycine max (L.) Merr.), are the richest source of isoflavones in human diet. Since there is not much current data on genetics of isoflavones in soybean, particularly in the aglycone form, elucidation of the mode of inheritance is necessary in order to design an efficient breeding strategy for the development of high-isoflavone soybean genotypes. Based on the isoflavone content in 23 samples of soybeans from four different maturity groups (00, 0, I and II), three crosses were made in order to determine the inheritance pattern and increase the content of total isoflavones and their aglycone form. Genotype with the lowest total isoflavone content (NS-L-146) was crossed with the low- (NS Zenit), medium (NS Maximus), and high- (NS Virtus) isoflavone genotypes. There were no significant differences in the total isoflavone content (TIF) between F2 populations, and there was no transgression among genotypes within the populations. Each genotype within all three populations had a higher TIF value than the lower parent (NS-L-146), while genotypes with a higher TIF value than the better parent were found only in the NS-L-146 × NS Zenit cross. However, significant differences in the aglycone ratio (ratio of aglycone to glycone form of isoflavones) were found between the populations. The highest aglycone ratio was found in the NS-L-146 × NS Maximus cross. The results indicate that the genetic improvement for the trait is possible.