A lightweight model based on improved PSO-SVR for predicting tobacco paper pot seedling growth
Abstract
This study aimed to develop an improved PSO-SVR model for the growth of tobacco paper pot seedlings under small-sample conditions. The model integrated environmental factors and substrate formulations to explore the nonlinear relationship between the environment and seedling growth. Two independent seedling cultivation experiments were conducted, yielding 480 observational records nested within 24 independent experimental plots and four growth stages. These data included key indicators such as root number, plant height, shoot dry weight, and chlorophyll a content. The experiment used three different plant ash ratios (20%, 40%, and 60%) and a control group. One-way analysis of variance (ANOVA) was used to identify sensitivity differences among the indicators. An improved PSO-SVR model was established. This model used adaptive inertia weight, dynamic learning factors, and a hybrid kernel function to improve parameter optimization and the model’s ability to fit nonlinear data. The results showed that the PSO-SVR model performed best on the independent validation set. The R2 values for root number, plant height, shoot dry weight, and chlorophyll a content were 0.923, 0.976, 0.958, and 0.919, respectively. Its prediction accuracy was much higher than that of MLR, Lasso, kNN, RF, GBM, SVR, MLP, and GPR models. The experiments also showed that a proper amount of plant ash (20%) could improve root activity and shoot growth, while a high amount (60%) inhibited early growth. This study provides a high-accuracy prediction framework for small-sample seedling production. It offers a reliable theoretical basis and technical guidance for substrate optimization, seedling quality assessment, and managed nursery cultivation.
Related
- Melatonin pretreatment promotes the growth and physiological metabolism of tobacco seedling roots under low light
- Agri-PIDNet-S: Lightweight semantic segmentation for field-structure and obstacle perception in seedling-stage cotton fields
- Early Seedling Growth Responses to Gluconacetobacter diazotrophicus Inoculation in Sugarcane: A Multivariate and Semi-Mechanistic Analysis
- Concentration-Dependent Effects of Artemisia annua Aqueous Extract on Seed Germination, Seedling Growth, and Physiological Responses of Solanum rostratum
- Storage-Dependent Responses of KDML 105 Rice (Oryza sativa L.) Seeds Across Dynamic Magnetic-Field Treatment Conditions: Germination, Seedling Growth, and Dual-Phase Hydration Modelling
BibTeX
@article{guo2026lightweight,
title = {{A lightweight model based on improved PSO-SVR for predicting tobacco paper pot seedling growth}},
author = {Ting Guo and Shuaihua Hao and Chengtian Zhu and Songbao Guo and Jitong Xu and Juxin Huo and Weiming Wang and Junxiang Tang and Cailing Liu},
journal = {Frontiers in Plant Science},
year = {2026},
month = nov,
doi = {10.3389/fpls.2026.1762905},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1762905},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A lightweight model based on improved PSO-SVR for predicting tobacco paper pot seedling growth
AU - Ting Guo
AU - Shuaihua Hao
AU - Chengtian Zhu
AU - Songbao Guo
AU - Jitong Xu
AU - Juxin Huo
AU - Weiming Wang
AU - Junxiang Tang
AU - Cailing Liu
PY - 2026
DA - 2026/11/05
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1762905
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1762905
AB - This study aimed to develop an improved PSO-SVR model for the growth of tobacco paper pot seedlings under small-sample conditions. The model integrated environmental factors and substrate formulations to explore the nonlinear relationship between the environment and seedling growth. Two independent seedling cultivation experiments were conducted, yielding 480 observational records nested within 24 independent experimental plots and four growth stages. These data included key indicators such as root number, plant height, shoot dry weight, and chlorophyll a content. The experiment used three different plant ash ratios (20%, 40%, and 60%) and a control group. One-way analysis of variance (ANOVA) was used to identify sensitivity differences among the indicators. An improved PSO-SVR model was established. This model used adaptive inertia weight, dynamic learning factors, and a hybrid kernel function to improve parameter optimization and the model’s ability to fit nonlinear data. The results showed that the PSO-SVR model performed best on the independent validation set. The R2 values for root number, plant height, shoot dry weight, and chlorophyll a content were 0.923, 0.976, 0.958, and 0.919, respectively. Its prediction accuracy was much higher than that of MLR, Lasso, kNN, RF, GBM, SVR, MLP, and GPR models. The experiments also showed that a proper amount of plant ash (20%) could improve root activity and shoot growth, while a high amount (60%) inhibited early growth. This study provides a high-accuracy prediction framework for small-sample seedling production. It offers a reliable theoretical basis and technical guidance for substrate optimization, seedling quality assessment, and managed nursery cultivation.
SN - 1664-462X
ER -