南方农业学报2026，Vol.57Issue(2)：378-387,10.DOI:10.3969/j.issn.2095-1191.2026.02.007
基于高光谱融合信息的火龙果遥感估产方法
Remote sensing method for pitaya yield estimation based on hyperspectral fusion information
摘要
Abstract
[Objective]This study aimed to investigate remote sensing method for pitaya yield estimation based on hy-perspectral fusion information,thereby providing technical support for local governments to optimize agricultural indus-trial structure,guide agricultural production,and promote high-quality development of the fruit industry.[Method]Using single pitaya plants at full fruiting stage as the experimental subjects,the imaging and non-imaging hyperspectral informa-tion was fused,and the continuous projection algorithm(SPA)was employed to select yield-sensitive bands as indepen-dent variables and ground-measured yield per plant as the response variable.At a ratio of 2∶1,126 sampling sites were di-vided into the modeling set(84 sites)and validation set(42 sites),and yield inversion models based on multiple linear regression(MLR),partial least squares regression(PLSR),support vector regression(SVR),and lion swarm optimiza-tion algorithm-support vector regression(LSOA-SVR)were established to evaluate estimation accuracy of single-sensor and multi-sensor fusion models.[Result]The imaging and non-imaging hyperspectral reflectance feature curves of pitaya plant canopy were basically consistent,with lower reflectance in the visible light region and higher reflectance in the near-infrared region.Within the visible light region,the green bands showed a reflection peak,while the red and blue bands showed absorption troughs.Reflectance of the canopy in non-imaging hyperspectral near-infrared region was negatively correlated with yield per plant,with low-yield plants showing higher spectral reflectance,and the imaging and non-imaging hyperspectral reflectance of canopy were 44.6%and 63.5%respectively.The extremely significant correlated(P<0.01)bands between imaging and non-imaging hyperspectral reflectance of fruit as well as pitaya yield per plant were mainly concentrated in 398-704 nm.According to pitaya yield inversion models based on single-sensor data,the MLR model had the lowest accuracy,followed by the PLSR model,and the SVR model was the best.Based on the four models based on multi-sensor fusion data,the SVR model had better accuracy than the MLR and PLSR models,and the LSOA-SVR model after lion swarm optimization algorithm further improved the performance of prediction.[Conclusion]By fus-ing imaging and non-imaging hyperspectral information,multi-source data collaboration can significantly improve the ac-curacy of remote sensing inversion for yield of pitaya at full fruiting stage.In yield inversion models based on single-sensor data,the nonlinear model SVR outperforms the linear models PLSR and MLR.As applying LSOA in parameter op-timization of SVR model,the proposed LSOA-SVR method can effectively improve prediction accuracy of the remote sensing inversion model for pitaya yield per plant.关键词
火龙果/高光谱遥感/反演模型/融合信息/机器学习Key words
pitaya/hyperspectral remote sensing/inversion model/fusion information/machine learning分类
农业科技引用本文复制引用
舒田,郭松,许元红,陈智虎,赵泽英..基于高光谱融合信息的火龙果遥感估产方法[J].南方农业学报,2026,57(2):378-387,10.基金项目
贵州省科研机构创新能力建设专项(黔科合服企[2021]15号) （黔科合服企[2021]15号）
贵州省科学技术基金项目(黔科合基础-ZK[2021]一般130号) Project of Guizhou Scientific Research Institution Innovation Ability Construction(QKHFQ[2021]15) （黔科合基础-ZK[2021]一般130号）
Guizhou Science and Technology Foundation(QKHJC-ZK[2021]Yiban 130) （QKHJC-ZK[2021]Yiban 130）
