基于结实率阈值与机器学习的水稻稳产筛选——以罗定区域试验为例
1. 罗定市农业发展中心, 广东罗定 527200
2. 广东省农业科学院水稻研究所, 广东广州 510640
| 摘 要： | 【目的】构建适用于双季稻产区的机器学习辅助稳产筛选体系，为品种审定与农技推广提供数据化辅助参考。【方法】提出一种规则阈值与机器学习协同的双轨筛选框架，以2022—2024年广东省罗定试点3年6造230个有效参试材料为对象，通过田间调查与考种获取产量、结实率、每穗实粒数、千粒重、株高、全生育期等核心农艺性状。构建双轨框架：第一模块以规则阈值（结实率≥68%且对照增产≥0%）进行稳产初筛，并以K近邻（KNN，K=5）二分类做一致性核查；第二模块采用逻辑回归（LR）三分类进行产量预测，以重复交叉验证评估性能。【结果】2023年晚造因台风引发病虫害，较2022年晚造减产52.6%。规则阈值从33个参试材料中筛出24个稳产材料（规则检出率72.7%），平均结实率77.3%；KNN二分类与规则生成标签的内部一致率为72.7%（属内部一致性核查，非独立预测）。结实率与产量Spearman ρ=0.411（P<0.001）为关键关联因子。LR三分类重复交叉验证准确率58.9%±6.5%，约为随机基线（33.3%）的1.8倍；KNN稳产二分类交叉验证准确率66.1%±6.4%。。上述关键正相关因子的显著性经Bonferroni校正（α′=0.05/6≈0.0083）后仍成立，表明结论对多重比较不敏感。【结论】双轨框架将规则初筛与数据驱动分类相结合，可在台风影响年份辅助识别稳产材料，为罗定及生态相似区域提供低成本技术方案；受单一试点限制，跨区域适应性有待多点验证。 |
| 关 键 词： | 水稻; 品种筛选; 稳产性; 结实率; 机器学习 |
| DOI： | 10.57237/j.jaf.2026.01.002 |
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收稿日期: 2026-07-27
接受日期: 2026-08-13
在线出版日期: 2026-09-07
Stable-Yield Rice Variety Screening Based on Seed-Setting Rate Thresholds and Machine Learning: A Case Study of the Luoding Regional Trial
1. Luoding Agricultural Development Center, Luoding 527200, China
2. Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| Abstract: | [Objective] To develop a machine-learning-assisted stable-yield screening framework for double-cropping rice regions, providing a data-driven auxiliary reference for variety certification and agricultural extension. [Method] Proposing a dual-track screening framework that integrates rule-based thresholds with machine learning, based on 230 valid trial materials spanning 3 years and 6 cropping seasons (2022-2024) at the Luoding regional trial site, core agronomic traits including yield, seed-setting rate, filled grains per panicle, 1000-grain weight, plant height, and growth duration were obtained through field investigation and laboratory testing. A dual-track framework was constructed: Track I used a rule-based threshold (seed-setting rate >=68% and yield increase over control >=0%) for preliminary screening, with K-nearest neighbor (KNN, K=5) binary classification as a consistency check; Track II employed logistic regression (LR) three-class classification for yield prediction, evaluated by repeated cross-validation. [Result] The 2023 late season, affected by typhoon-induced pest outbreaks, experienced a 52.6% yield reduction. The rule-based threshold screened 24 stable-yield materials (detection rate 72.7%), with a mean seed-setting rate of 77.3%, and KNN classification agreed with the rule at 72.7% (this is an internal consistency check between rule and classifier, not an independent prediction, since seed-setting rate is both a rule condition and a model input feature). Spearman rank correlation between seed-setting rate and yield was rho=0.411 (P<0.001). LR achieved repeated cross-validation accuracy of 58.9%+/-6.5% (about 1.8x the 33.3% random baseline); KNN binary classification accuracy was 66.1%+/-6.4%. The above key correlation factors remained significant after Bonferroni correction (alpha'=0.05/6 approx 0.0083), indicating robustness to multiple comparisons. [Conclusion] The dual-track framework integrating rule-based screening with data-driven classification can assist in identifying stable-yield materials under typhoon-impacted conditions, providing a low-cost technical solution for Luoding and ecologically similar regions; cross-regional adaptability awaits multi-site validation. |
| Keywords: | Rice; Variety Screening; Yield Stability; Seed-setting Rate; Machine Learning |
基金
| 1. | 省级乡村振兴战略专项 (种业振兴行动) 项目——农作物品种试验 (No. 2024-445381-69010000-0001) |
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