Guizhou Minzu University, Guiyang, China
More than half of the world's population is dominated by rice. In the context of reduced cultivated land and increasingly scarce water resources, increasing rice production has become an urgent problem in order to meet the needs of world's growing population. On the basis of previous work, this paper combines the entropy weight method with the fuzzy comprehensive evaluation method to expand the method of comprehensive evaluation of rice quality. The main purpose is to establish an entropy weight fuzzy comprehensive evaluation model and apply it to comprehensive evaluation of rice quality of japonica rice variety in 12 different producing areas. At the same time, identity the marker genotypes of parental yield traits, and found 31 marker genotypes are significantly correlated with the yield of parental yield traits. The two marker genotypes are related to the six traits of the parent; two marker genotypes are related to the 5 traits of parental at the same time; there are four marker genotypes associated with the four traits of the male parent; There are five marker genotypes associated with maternal features; there are three marker genotypes associated with the parental and maternal genetic traits; There are 15 marker genotypes associated with parental traits of individual traits. The marker genotype of the RM23~150/160 was positive for 4 genotype effects. The number of per panicle, daily yield per plant, ear length and number of secondary branches increased by 12.1%, 11.3%, 10.4% and 14.9%, respectively. The results showed that the whiteness, different cultivar rate, defect rate and amylose content may be the main indicators affecting differences in quality of various varieties’ rice. Among the 12 varieties, the comprehensive quality of Ji japonica in 88 and Ji japonica in 83 was better, evaluation level is I; long white 19 and long white 25 have overall poor quality performance and the evaluation level is V.
Entropy Weight, Fuzzy Comprehensive Evaluation Method, Rice Quality, Variety Japonica Rice
Xuanting Huang. Entropy Weight Fuzzy Comprehensive Evaluation in Screening of Japonica Rice Quality. Academic Journal of Agricultural Sciences (2022), Vol. 3, Issue 4: 74-87. https://doi.org/10.38007/AJAS.2022.030406.
 Zhang H Y, Pu J, Wang J Q, et al. An Improved Weighted Correlation Coefficient Based on Integrated Weight for Interval Neutrosophic Sets and its Application in Multi-criteria Decision-making Problems. International Journal of Computational Intelligence Systems, 2015, 8(6):1027-1043.
 Zhang T J, Ren J H, Yu S H, et al. Entropy Weight-Fuzzy Comprehensive Evaluation Method of the Safety Evaluation of Water Inrush. Advanced Materials Research, 2014, 868:300-305.
 Zhang G, Liu R, Yang C, et al. Application of Fuzzy Comprehensive Evaluation Method Based on Entropy Weight Theory in Evaluation of Salt Tolerance of Cotton. Agricultural Science & Technology, 2014,9(6):277-444.
 Liu X K, Yang F, Gao J, et al. Fuzzy Comprehensive Quality Evaluation of Chopped Carbon Fiber Based on Entropy Weight. Journal of South China University of Technology, 2016 ,21(5):457967.
 Wen J, Wei B, Xiang L, et al. Intuitionistic Fuzzy Power Aggregation Operator Based on Entropy and Its Application in Decision Making. International Journal of Intelligent Systems, 2017, 33(1):997.
 Wu H M, Wei Z, Liang C H, et al. Application of fuzzy comprehensive evaluation method based on entropy weight to evaluate pond water quality. Agricultural Science & Technology, 2014,9(6):277-444.
 Liang-Wu Y U, Liu D F, Fang Y L, et al. Fuzzy Comprehensive Evaluation Based on Entropy Weight Method for Hydraulic Fluid Contamination Level. Chinese Hydraulics & Pneumatics, 2018,9(6):77-114.
 Siddiqui Z, Tyagi K. A Novel Study on Service Selection Effort Estimation in SOA based Applications Powered by Information Entropy Weight Fuzzy Comprehensive Evaluation Model. Iet Software, 2017, 12(2):283.
 Yan J, Zhang T, Zhang B, et al. Application of Entropy Weight Fuzzy Comprehensive Evaluation in Optimal Selection of Engineering Machinery Isecs International Colloquium on Computing, Communication, Control, & Management. Journal of Southwest Agricultural University,2015,3(5):12-34.
 Wen S, Zeng Z, Huang T, et al. Exponential Adaptive Lag Synchronization of Memristive Neural Networks via Fuzzy Method and Applications in Pseudorandom Number Generators. IEEE Transactions on Fuzzy Systems, 2014, 22(6):1704-1713.
 He Z, Zheng Q. Fuzzy mathematics-based comprehensive evaluation of atmospheric environmental quality in Chongqing. Journal of Southwest Agricultural University, 2015,7(5):112-145.
 Feng L, Ye Y, Song B, et al. Evaluation of urban suitable ecological land based on the minimum cumulative resistance model: A case study from Changzhou, China. Ecological Modelling, 2015, 318(1):194-203.
 Wen J, Wei B. Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making. International Journal of Systems Science, 2017, 49(4):1-13.
 Wang T. Risk Evaluation of Wartime Equipment Supply Chain Based on TOPSIS Method with Fuzzy Ameliorated Entropy Weight. Computer & Digital Engineering, 2015,22(9):45935.
 Rodríguez L, Castillo O, Soria J, et al. A Fuzzy Hierarchical Operator in the Grey Wolf Optimizer Algorithm. Applied Soft Computing, 2017, 5(7):315-328.
 Song J, Liu Y, Song D. Multi-grade Fuzzy Comprehensive Evaluation of BOT Projects Service Quality Based on Fuzzy Entropy Weight Coefficient Method . Business Intelligence & Financial Engineering. 2016,43(9):32121.
 Wang J, Gao Y, Qiu J, et al. Sliding mode control for non-linear systems by Takagi–Sugeno fuzzy model and delta operator approaches. Iet Control Theory & Applications, 2017, 11(8):1205-1213.
 Xian S, Jing N, Li T, et al. A Novel Approach Based on Intuitionistic Fuzzy Combined Ordered Weighted Averaging Operator for Group Decision Making. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018, 26(3):77-78.
 Zhang J H, Xia J J, Garibaldi J M, et al. Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets.. Comput Methods Programs Biomed, 2017, 14(4):147-163.
 Huang D, Chen C, Sun G, et al. Recognition and Diagnosis Method of Objective Entropy Weight for Power Transformer Fault. Automation of Electric Power Systems, 2017, 41(12):206-211.