农业工程2026，Vol.16Issue(7)：18-26,9.DOI:10.19998/j.cnki.2095-1795.202601055
基于多源数据融合的苏打盐碱地变量施肥智能分级研究
Intelligent classification of variable rate fertilization for soda salt-alkali soil based on multi-source data fusion
摘要
Abstract
To achieve precise management of soda saline-alkali soil in northeast China and variable rate application of organic fertilizers,a multi-source data fusion deep learning method hybrid model driven by fertilizer recommendation index(FRI)has been proposed.A FRI-CNN-MLP hybrid model was constructed by integrating 2023 growing season Sentinel-2 time-series remote sensing data,based normalized difference vegetation index(NDVI)and soil-adjusted vegetation index(SAVI)with soil cation exchange capacity(CEC)and pH data from SoilGrids database.This model has achieved four-level classification of fertilization application intensity within plastic-mulched areas of saline-alkali soil.FRI labels for model training were generated based on expert rules derived from current technical standards.Prediction results demonstrated that light fertilizer application index(accounting for 70.53%)was prodominant in study area,while moderate and heavy application were concentrated in low-lying peripheral areas,exhibiting a heavier application at periphery and lighter application at center.Compared with baseline method,model achieved an overall accuracy rate of 89.7%,and macro average F1 score of 0.85,outperforming traditional threshold method and random forest model.FRI-CNN-MLP hybrid model effectively integrates spatiotemporal-soil features,overcoming strong subjectivity and poor generalization of traditional threshold methods.It can directly de-termine organic fertilizer application rates,providing technical support for intelligent saline-alkali soil improvement.关键词
苏打盐碱地/多源数据融合/变量施肥/深度学习/施肥推荐指数Key words
soda saline-alkali soil/multi-source data fusion/variable rate fertilization/deep learning/fertilization recommendation index分类
农业科技引用本文复制引用
刘大欣,胡伟,吴宝广,周德义,于春生..基于多源数据融合的苏打盐碱地变量施肥智能分级研究[J].农业工程,2026,16(7):18-26,9.基金项目
国家重点研发计划项目(2023YFD1500605) （2023YFD1500605）
