中国农业科学2025，Vol.58Issue(9)：1719-1734,16.DOI:10.3864/j.issn.0578-1752.2025.09.004
基于多角度成像与机器学习的水稻叶面积精确估算
Multi-Angle Imaging and Machine Learning Approaches for Accurate Rice Leaf Area Estimation
摘要
Abstract
[Objective]Rice leaf area is a critical physiological metric that indicates photosynthetic efficiency,energy conversion,and dry matter accumulation capacity.This study aimed to develop a simple and efficient rice leaf area imaging system and prediction method,so as to provide a theoretical foundation and technical support for rapid and accurate leaf area measurement.[Method]The study utilized representative rice varieties—Xiushui 134(indica),Huanghuazhan(japonica),and Yongyou 1540(indica-japonica hybrid)—as experimental materials.Leaf area data were collected from the aboveground parts during critical growth periods,and both flat-overhead-view and side-view images were captured.Using the PlantScreen high-throughput modular plant phenotyping platform,morphological and color feature information was extracted.Based on these data,various feature selection methods(Pearson correlation coefficient,maximal information coefficient(MIC),and recursive feature elimination(RFE))combined with machine learning models(support vector regression(SVR),random forest regression(RFR),and XGBoost)and deep learning models(ResNet50,AlexNet,VGG,and SeNet)were employed to develop a simplified and efficient rice leaf area prediction model.[Result](1)An imaging approach that integrated flat-overhead and multi-angle side views significantly outperformed single-view methods for leaf area prediction,with R² values of 0.76-0.82 and coefficients of variation(CV)of 5.5％-13.7％,compared with R² values of 0.51-0.78 and CVs of 9.7％-27.5％for single views.The optimal system used one flat-overhead-view and one side-view image,achieving R²=0.79,root mean square error(RMSE)=95.3,mean absolute error(MAE)=77.02,and CV=6.5％.(2)Using MIC algorithm for key feature selection combined with the random forest regression model achieved excellent results(R²=0.84,RMSE=81.8,and MAE=63.3),noticeably outperforming other machine learning models.The deep learning model SeNet(R2=0.80,RMSE=98.1,and MAE=74.7)outperformed traditional ResNet50 and AlexNet models but showed no significant advantage over the MIC-RFR model.(3)Feature analysis indicated that the projected area and plant height from side-view images,as well as leaf perimeter and green-yellow characteristics from flat-overhead-view images,significantly contributed to leaf area prediction.The contribution of the side-view projected area(+117.4)was substantially greater than that of other features(ranging from 1.48 to 18.87).[Conclusion]This study employed a simple and efficient leaf area prediction imaging system(one flat-overhead-view combined with one side-view image),integrated with the MIC-RFR model,to meet the high-precision and stable prediction requirements for individual rice leaf area.This method provided a powerful tool and technical support for precision agriculture and crop breeding.关键词
多角度RGB图像/形态学特征/色彩特征/叶面积预测/机器学习/水稻Key words
multi-angle RGB image/morphological feature/color feature/leaf area prediction/machine learning/rice引用本文复制引用
王爱冬,李瑞杰,冯向前,洪卫源,李子秋,张晓果,王丹英,陈松..基于多角度成像与机器学习的水稻叶面积精确估算[J].中国农业科学,2025,58(9):1719-1734,16.基金项目
国家重点研发计划(2022YFD2300702-2)、国家水稻产业技术体系(CARS-01)、中国农业科学院科技创新工程重大科研任务(CAAS-ZDRW202001) （2022YFD2300702-2）
