Rice performance prediction to deficit irrigation using microsatellite alleles and artificial intelligence
نویسندگان
چکیده
Rice germplasm investigated as completely randomized design under flooding and deficit irrigation conditions. The results of the association analysis indicated that RM29, RM63, RM53 could be used for rice breeding programs to improve yields irrigation. highest accuracy performance prediction was 98.36 RFA (RFA) panicle length, flag leaf width, number primary branches, after that, MLP algorithm had better power than other algorithms. When a genotypes code considered criterion classify drought stress at reproductive stage, random forest best based on predictive (67.93), kappa value (0.514) root mean square error (0.293). Based artificial intelligence methods, presented predict response using microsatellite molecular data.
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ژورنال
عنوان ژورنال: Acta biologica Szegediensis /
سال: 2022
ISSN: ['1588-385X', '1588-4082']
DOI: https://doi.org/10.14232/abs.2022.1.37-46