Prediction of oil yield in sunflower using deep learning regression algorithm under normal and drought stress conditions

Abstract Accurate prediction of sunflower oil yield is essential for crop management and breeding decisions, particularly under water-limited environments. In this study, 100 pure oilseed sunflower lines were evaluated under normal and drought stress conditions across two consecutive growing seasons (2014–2016), and all morphological and physiological variables were measured directly in the field. Input variables were selected based on their previously demonstrated influence on grain yield and oil-related traits, while the complete two-year dataset was used for model training, validation, and testing following data augmentation, standardization, and 7-fold cross-validation. The predictive performance of multiple linear regression (MLR) and deep learning regression (DLR) models was compared using different combinations of input variables. The DLR model consistently demonstrated superior performance, achieving higher accuracy and lower prediction errors across all scenarios. The best results were obtained under drought stress with 11 input variables, where the DLR model achieved (R2 = 0.98, RMSE = 0.4, MSE = 0.16, MAE = 0.20 (train), R2 = 0.96, RMSE = 0.55, MSE = 0.31, MAE = 0.34 (test)), along with markedly lower RMSE and MAE values than MLR. Even when fewer variables were used, the DLR model maintained strong predictive ability, highlighting its capacity to learn complex nonlinear relationships and generalize from limited data. Overall, the findings underscore the potential of DLR as a robust predictive tool for estimating oil yield under contrasting environmental conditions and provide practical implications for genotype selection, harvest planning, and sunflower breeding strategies.