Affiliations: [a]
Department of Plant Production, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran
| [b]
Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran
Correspondence:
[*]
Corresponding author: Zahra Zinati, Department of Agroecology, College of Agriculture and Natural Resources of Darab, Shiraz University, Shiraz, Iran. Tel.: +98 9173369680; Fax: +987153546476; E-mails: [email protected]; ORCID: 0000-0001-9362-0417.
Abstract: BACKGROUND:The fig (Ficus carica L.) tree known for its tasty and nutritious fruits, is typically propagated by cutting. While previous studies have focused on the effects of different treatments and environmental conditions on fig cutting propagation, little attention has been paid to the specific role and association of biochemical properties in leaves, stem bark and fruit on the rooting process. OBJECTIVE:This research explores the complex relationship between 40 biochemical traits and the rooting ability of fig cuttings. To achieve this objective, various machine learning techniques were employed, such as a random forest model, feature importance analysis, linear regression, and principal component analysis (PCA). RESULTS:The random forest model showed significant predictive ability with a classification accuracy of 100%, supported by a high kappa statistic. Feature importance analysis identified a* (a colorimetric parameter in fruit), fruit trans-ferulic acid and leaf total flavonoids as the most influential traits in determining the rooting ability of cuttings. The robustness of these findings is supported by the high R-squared value (0.9002) and low error metrics (MAE 0.7554 and MSE 0.6980) of the linear regression model built on these important traits. In parallel, PCA indicated that a*, leaf total flavonoids and fruit trans-ferulic acid were the dominant traits in samples with lower rooting percentage. CONCLUSIONS:These identified biomarkers can be effectively used by fig breeders and growers to select and introduce fig cultivars with improved rooting ability.
Keywords: Cutting, Ficus carica L., linear regression model, principal component analysis, random forest model, rooting
