Establishment of a model for predicting yield of winter rye with a hybrid random forest-recursive feature elimination

LÜ Linyou; LI Jiahui; ZHAO Yan; MA Yan; HE Ziyuan

doi:10.13300/j.cnki.hnlkxb.2026.03.005

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Establishment of a model for predicting yield of winter rye with a hybrid random forest-recursive feature elimination

Agricultural artificial intelligence | 更新时间：2026-11-05

- Establishment of a model for predicting yield of winter rye with a hybrid random forest-recursive feature elimination
- Journal of Huazhong Agricultural University Vol. 45, Issue 3, Pages: 56-67(2026)
- 作者机构：
  
  1.辽宁工程技术大学环境科学与工程学院，阜新 123000
  2.辽宁省沙地治理与利用研究所，阜新 123000
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2026.03.005
  CLC： S512.5
- Received：23 November 2025，
  
  Published：25 May 2026
- 稿件说明：
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吕林有，李佳慧，赵艳，等.融合随机森林-递归特征消除的冬黑麦产量预测模型的建立［J］.华中农业大学学报，2026，45（3）：56‐67.

.Establishment of a model for predicting yield of winter rye with a hybrid random forest-recursive feature elimination[J].Journal of Huazhong Agricultural University,2026,45(03):56-67.
吕林有，李佳慧，赵艳，等.融合随机森林-递归特征消除的冬黑麦产量预测模型的建立［J］.华中农业大学学报，2026，45（3）：56‐67. DOI： 10.13300/j.cnki.hnlkxb.2026.03.005.

.Establishment of a model for predicting yield of winter rye with a hybrid random forest-recursive feature elimination[J].Journal of Huazhong Agricultural University,2026,45(03):56-67. DOI： 10.13300/j.cnki.hnlkxb.2026.03.005.

摘要

针对传统模型难以解析行距-播种量互作非线性效应的问题，通过双因素田间梯度试验，系统测定冬黑麦关键生育期生物学性状与产量构成要素指标，解析关键生育期群体动态与产量构成的互作效应，构建融合随机森林-递归特征消除的冬黑麦产量预测模型。田间试验结果显示，40 cm行距×600 g/40 m

播种量处理组合能够优化冠层透光率、平衡“源-库”关系及提升抗逆性，实现冬黑麦高产稳产的协同调控；在冬黑麦完全成熟期，系统采集不同处理组的植株样本，并采用标准化试验流程对关键农艺性状，包括穗长、穗质量、穗节数、株高、根长、有效分蘖数、叶片数以及单株种子数进行量化表征，共获得540组有效数据，以单株种子数为预测目标，构建了包含播种行距、播种量、穗长、穗质量、穗节数、株高、根长、有效分蘖数、叶片数等原始变量及6个衍生特征的多维特征集，构建递归特征消除的随机森林（random forest-recursive feature elimination，RF-RFE）模型，模型表现出优异的预测性能（

²=0.951，RMSE=2.28）和泛化能力（OOB-RMSE=4.33）。通过产量预测模型反演获得行距-播种量三维响应曲面，确定了高产参数为行距35~49 cm、播种量535~680 g/40 m

；边际效应揭示行距

45 cm时每增加1 cm减产1.23 kg，播种量

650 g/40 m

时边际收益递减。因此，建议生产实践中将行距控制在40~45 cm，播种量控制在600~650 g/40 m

，既可保证高产又留出操作容错空间。

Abstract

A two-factor field gradient experiment was conducted to solve the problems of traditional models being difficult to analyze the nonlinear effects of interaction between row spacing and sowing rate. The biological traits and yield components at the stages of key growth in winter rye were systematically measured. The patterns of interaction between population dynamics and yield

components during these stages were analyzed to construct a recursive gradient fusion-based random forest model for predicting the yield of winter rye. The results of field experiment showed that the combination of a row spacing of 40 cm and a sowing rate of 535-680 g/40 m

can optimize the light transmittance of canopy， balance the “source sink” relationship， and improve the resistance to stress， achieving coordinated regulation of high and stable yield of winter rye. Samples of plant were systematically collected from different treatment groups at the stage of full maturity in winter rye. A standardized experimental procedure was used to quantitatively characterize key agronomic traits including spike length， spike mass， number of spike nodes， plant height， root length， effective tiller count， leaf count， and seeds per plant. 540 sets of valid data were obtained. A multidimensional feature set encompassing sowing row spacing， seeding rate， spike length， spike mass， number of spike nodes， plant height， root length， effective tiller count， leaf count， and six derived features was constructed using seeds per plant as the prediction target. A hybrid random forest-recursive feature elimination （RF-RFE） with excellent predictive performance （

²=0.951， RMSE=2.28） and generalization ability （OOB-RMSE=4.33） was constructed. The model for predicting yield was used to invert and obtain a three-dimensional response surface for row spacing and seeding rate to identify the high-yield parameters as row spacing ranging from 35 to 49 cm and seeding rate between 535 and 680 g/40 m

. The results of marginal effect showed that a 1 cm increase in row spacing beyond 45 cm led to a yield decrease of 1.23 kg， and the marginal benefit decreased as the seeding rate exceeded 650 g/40 m

. Therefore， it is recommended to control the row spacing within 40-45 cm and the seeding rate between 600-650 g/40 m

in the practice of production， which can ensure high yield and leave room f

or the tolerance of operational error.

关键词

Keywords

references

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