An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

• Published in: Scientific reports Vol. 12; no. 1; p. 20514
• Main Authors: Khalaki, Masoomeh Abbasi, Jahantab, Esfandiar, Abdipour, Moslem, Moameri, Mehdi, Ghorbani, Ardavan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-11-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647; 631/449; Algorithms; Animal models; Animals; Festuca; Festuca ovina; Flowering; Genotypes; Grasses; Humanities and Social Sciences; Leaf area; Machine Learning; Mountain regions; multidisciplinary; Neural networks; Neural Networks, Computer; Physical characteristics; Plant Breeding; Principal components analysis; Regression analysis; Science; Science (multidisciplinary); Sensitivity analysis; Sheep; Water; Water content
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-25110-8

Festuca ovina
L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R
2
). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R
2
scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.