文献类型： 外文期刊

作者： Qureshi, S. H. ¹ ; Javaid, A. ² ; Saleem, S. ² ; Ilyas, M. K. ² ; Khan, M. A. ³ ; Alam, B. ⁴ ; Aljowaie, R. M. ⁵ ; Elshikh, M. S. ⁵ ; Iqbal, R. ⁶ ;

作者机构： 1.Islamia Univ Bahawalpur Pakistan, Dept Comp Sci & Informat Technol, Bahawalpur, Pakistan

2.Natl Agr Res Ctr, Pk Rd, Islamabad, Pakistan

3.Islamia Univ Bahawalpur, Fac Agr & Environm, Dept Plant Breeding & Genet, Bahawalpur 63100, Pakistan

4.Chinese Acad Agr Sci, State Key Lab Cotton Biol, Key Lab Biol & Genet Breeding Cotton, Minist Agr,Inst Cotton Res, Anyang, Peoples R China

5.King Saud Univ, Coll Sci, Dept Bot & Microbiol, PO 2455, Riyadh 11451, Saudi Arabia

6.Islamia Univ Bahawalpur, Fac Agr & Environm, Dept Agron, Bahawalpur, Pakistan

7.Western Caspian Univ, Dept Life Sci, Baku, Azerbaijan

关键词： additive; genetic images; deep learning; physiological; nutritional

期刊名称：APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH （ 影响因子：0.6； 五年影响因子：0.8 ）

ISSN： 1589-1623

年卷期： 2025 年 23 卷 2 期

页码：

收录情况： SCI

摘要： The early selection of pure lines against stalk rot and inheritance pattern of physiological and nutritional traits under disease stress is the main problem faced by the breeders in developing countries. One hundred maize lines were assessed for stalk rot using both artificial and natural inoculation. Six generations (P1, P2, F1, F2, BC1, and BC2) produced from the pure lines showing consistent resistance (Y11, EL7) and susceptible (DR59, DR69) responses during two growing seasons. Genetic images of these lines were obtained using DNA-based makers (SSRs) by performing polymerase chain reaction in the department of Medicinal Chemistry, University of Minnesota, USA. Studied traits showed the dominating impact of additive gene action in both seasons. Nutritional traits (protein, oil and starch contents) mostly exhibited negative correlation with lesion length while other showed non-significant correlation during spring and autumn. A deep learning model (Inception-V3) was trained with genetic images to distinguish between resistant and susceptible lines. The model showed 95% accuracy in resistant line detections. Considering the impact of the illness on physiological and nutritional traits, the current study favored phenotypic based selection in physiological traits. The study also showed the adverse impact of disease on nutritional traits. The current research not only aids in the management of the disease in high-yielding varieties, but also aids in identifying the resistant lines earlier against stalk rot using image processing model.