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.

Introduction/Objective. The Sarcopenia Quality of Life (SarQoLR) questionnaire is a patient-reported outcome measure specific to sarcopenia. The objective was to translate the SarQoLR questionnaire from English into Serbian and to investigate its psychometric performance. Methods. A five-stage forward-backward methodology with pre-test was used to translate the questionnaire. The validation sample in this study consisted of elderly, community-dwelling volunteers of both sexes. Three methods were used to screen for and diagnose sarcopenia: the SARC-F questionnaire (high/ low risk), low handgrip strength [probable sarcopenia in the European Working Group on Sarcopenia in Older People (EWGSOP2) algorithm], and the complete EWGSOP2 criteria. We investigated the questionnaire?s discriminative power, internal consistency, construct validity, and floor and ceiling effects. Results. The SarQoLR questionnaire was translated into Serbian. The validation study included 699 participants. In total, 200 participants were considered to be at high risk of sarcopenia by the SARC-F, 84 were diagnosed with low handgrip strength and 12 were confirmed to be sarcopenic. We did not find significantly lower overall QoL scores using the EWGSOP2 criteria (60.31 vs. 64.60; p = 0.155). We did find lower scores for the probably sarcopenic group (52.80 vs. 65.50; p < 0.001) and the high-risk group (50.91 vs. 69.02; p < 0.001). The Cronbach?s ? coefficient was 0.87, indicating a high internal consistency. Construct validity was adequate, with 75% of hypotheses on expected correlations with the SF-36 and EQ-5D questionnaires confirmed. No floor or ceiling effects were observed. Conclusion. We successfully translated the SarQoLR into Serbian, and showed that it is a valid tool for measuring QoL in the community-dwelling elderly.

Efficient water supply and timely detection of drought stress in crops to increase yields is an important task considering that agriculture is the primary consumer of water globally. This is particularly significant for plants such as sunflowers, which are an important source of quality edible oils, essential for human nutrition. Traditional detection methods are labor-intensive, time-consuming, and rely on advanced sensor technologies. We introduce an innovative approach based on neural networks and transfer learning for drought stress detection using a novel dataset including 209 non-invasive rhizotron images and 385 images of manually cleaned sections of sunflowers, subjected to normal watering or water stress. We used five neural network models: VGG16, VGG19, InceptionV3, DenseNet, and MobileNet, pre-trained on the ImageNet dataset, whose performance was compared to select the most efficient architecture. Accordingly, the most efficient model, MobileNet, was further refined using different data augmentation mechanisms. The introduction of targeted data augmentation and the use of grayscale images proved to be effective, demonstrating improved results, with an F1 score and an accuracy of 0.95. This approach encourages advances in water stress detection, highlighting the value of artificial intelligence in improving crop health monitoring and management for more resilient agricultural practices.