福建农业学报2026，Vol.41Issue(2)：159-169,11.DOI:10.19303/j.issn.1008-0384.2026.02.003
基于图像特征衍生的水稻氮素精准监测研究
Photographic Images for Accurate Estimating Rice Nitrogen Content
摘要
Abstract
[Objective]To rapidly and accurately monitor the nitrogen nutrition status of rice and determine the optimal nitrogen application rate for high yield and quality.[Methods]Field experiments were conducted over two years(2022-2023)using two locally dominant rice varieties:the conventional early indica rice'zhongjiazao17'and the hybrid rice'changliangyou173'.Four nitrogen fertilizer levels(0,75,150,225 kg·hm-2,denoted as N0,N1,N2,and N3,respectively)were established.Digital camera(Canon EOS 100D,resolution 72 pixels per inch)was used to acquire rice canopy images and corresponding nitrogen nutrition data.Nitrogen nutrition monitoring models were constructed based on image features and their derived parameters.[Results]The percentage of rice pixels(PRP)in the image and its derived features showed high correlations with leaf area index(LAI),above ground biomass(AGB),and nutrition accumulation(PNA),with the best model prediction performance observed at the jointing stage.Further analysis revealed that polynominal functions based solely on RPR could effectively predict LAI,AGB,and PNA,with coefficients of determination(R2)of 0.76,0.74,and 0.79,respectively(P＜0.01),and the root mean square error(RMSE)for the model validation was 0.32 g·m-2,22.30 g·m-2,and 2.54 g·m-2,and the relative root mean square error(RRMSE)was 8.25%,7.61%,and 26.49%,respectively.In contrast,high-order exponential function derived from PRP provided superior predictions for LAI,AGB,and PNA,with R2 of 0.89,0.92,and 0.93,respectively(P＜0.01),RMSE values of 0.16 g·m-2,3.71 g·m-2,0.57 g·m-2,and RRMSE values of 4.20%,1.27%,and 5.98%(all＜10%),indicating excellent model stability.[Conclusion]Overall,the feature derivation strategy effectively improves the prediction accuracy and stability of the models,demonstrating significant application values for monitoring rice nitrogen nutrition.关键词
图像/特征衍生/水稻/氮素营养Key words
photographic image/feature derivatives/rice/nitrogen content分类
农业科技引用本文复制引用
叶春,舒时富,孙滨峰,吴罗发..基于图像特征衍生的水稻氮素精准监测研究[J].福建农业学报,2026,41(2):159-169,11.基金项目
国家自然科学基金项目(32460442) （32460442）
国家重点研发计划项目(2024YFD2000205-3) （2024YFD2000205-3）
中央引导地方科技发展资金项目(CAAMS-JX202501) （CAAMS-JX202501）
