The research on LAI estimation of multiple rice varieties based on multi-modal data fusion and machine learning algorithms

doi:10.1016/j.jia.2026.02.002

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Aidong Wang¹^*, Ruijie Li^1,²^*, Xiangqian Feng^1,², Ziqiu Li¹, Hengjie Gao¹, Huaxing Wu¹, Danying Wang¹, Song Chen¹^#

¹China National Rice Research Institute, State Key Laboratory of Rice Biology and Breeding, Hangzhou 311400, China

²College of Agriculture, Yangtze University, Jingzhou 434025, China

Highlights

Develops a multimodal data fusion framework for improved multi-variety LAI estimation accuracy.

Integrating canopy structure with deep features significantly outperforms traditional vegetation index methods.

DSM serves as a crucial feature in enhancing LAI model precision.

Abstract
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摘要

由于密集冠层条件下的光谱饱和效应以及不同水稻品种间的生理差异，利用光学遥感技术准确估算多品种水稻叶面积指数（LAI）仍面临挑战。为解决这一问题，本研究构建了一种多模态数据融合框架，该框架整合了无人机（UAV）获取的RGB影像与多光谱影像，并进一步结合数字表面模型（DSM）衍生的特征、植被指数（VIs）、纹理特征以及图像深度特征。基于60个水稻品种的田间数据，我们评估了四种机器学习模型在LAI估算中的表现。结果表明，多模态融合方法显著优于传统的植被指数的方法。其中，随机森林回归模型取得了最佳性能（R² = 0.76，RMSE = 0.57），相较于基准模型，R²提高了26–58%。基于SHAP特征重要性分析显示，DSM特征、高度分层植被指数以及深度特征是提升模型精度的关键贡献因素。本研究证实，引入冠层结构信息和深度特征能够有效缓解饱和效应，并增强模型在不同品种间的泛化能力。所提出的方法为高通量LAI估算提供了稳健高效的解决方案，可服务于精准农业与水稻育种项目。

Abstract

Accurate estimation of Leaf Area Index (LAI) in multi-variety rice using optical remote sensing remains challenging due to spectral saturation under dense canopy conditions and inter-varietal physiological differences. To address this, we developed a multimodal data fusion framework integrating RGB and multispectral imagery acquired by unmanned aerial vehicles (UAVs), combined with features derived from Digital Surface Models (DSM), vegetation indices (VIs), texture, and depth representations. Using field data collected across 60 rice varieties, four machine learning models were evaluated for LAI estimation. Our results demonstrate that multimodal fusion substantially outperforms conventional VI-based approaches. Among them, the Random Forest Regression (RFR) model achieved optimal performance (R²=0.76, RMSE=0.57), representing a 26–58% improvement in R² over baseline models. SHAP-based feature importance analysis identified DSM feature, height-stratified vegetation indices, and depth features as key contributors to model accuracy. This study establishes that incorporating canopy structural information and deep features mitigates saturation effects and enhances generalizability across varieties. The proposed approach offers a robust and efficient solution for high-throughput LAI estimation, supporting applications in precision agriculture and rice breeding programs.

Keywords: leaf area index machine learning canopy structure depth feature

Online: 05 February 2026

Fund:

This study was funded by the National Natural Science Foundation of China (32572448), the Joint Fund of the National Key Research and Development Program of China “ Research and demonstration of integrative approaches to synergistically improve yield, quality and efficiency in rice production in Southern China” (2022YFD2300700), Zhejiang “Ten thousand talents” plan science and technology innovation leading talent project (2020R52035), the Open Project Program of the State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute (2023ZZKT20402), and the Agricultural Science and Technology Innovation Program(ASTIP), the Central Public-Interest Scientific Institution Basal Research Fund, China (CPSIBRF-CNRRI-202410).

About author: Aidong Wang, E-mail: wangaidongjy@163.com; Ruijie Li, E-mail: 17671059241@163.com; #Correspondence Song Chen, E-mail: chensong02@caas.cn * These authors contributed equally to this work.

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Aidong Wang, Ruijie Li, Xiangqian Feng, Ziqiu Li, Hengjie Gao, Huaxing Wu, Danying Wang, Song Chen. 2026. The research on LAI estimation of multiple rice varieties based on multi-modal data fusion and machine learning algorithms. Journal of Integrative Agriculture, Doi:10.1016/j.jia.2026.02.002

Araus J L, Cairns J E. 2014. Field high-throughput phenotyping: The new crop breeding frontier. Trends in Plant Science, 19, 52-61.

Bao W, Yang X, Liang D, Hu G, Yang X. 2021. Lightweight convolutional neural network model for field wheat ear disease identification. Computers and Electronics in Agriculture, 189, 106367.

Baret F, Guyot G. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35, 161-173.

Carmona F, Rivas R, Fonnegra D C. 2015. Vegetation Index to estimate chlorophyll content from multispectral remote sensing data. European Journal of Remote Sensing, 48, 319-326.

Che Y, Wang Q, Xie Z, Zhou L, Li S, Hui F, Wang X, Li B, Ma Y. 2020. Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography. Annals of Botany, 126, 765-773.

Chen Q, Zheng B, Chenu K, Hu P, Chapman S C. 2022. Unsupervised plot-scale LAI phenotyping via UAV-based imaging, modelling, and machine learning. Plant Phenomics, 2022, 9768253.

Chen Y, Jiao S, Cheng Y, Wei H, Sun L, Sun Y. 2022. LAI-NOS: An automatic network observation system for leaf area index based on hemispherical photography. Agricultural and Forest Meteorology, 322, 108999.

Cheng J, Yang H, Qi J, Sun Z, Han S, Feng H, Jiang J, Xu W, Li Z, Yang G, Zhao C. 2022. Estimating canopy-scale chlorophyll content in apple orchards using a 3D radiative transfer model and UAV multispectral imagery. Computers and Electronics in Agriculture, 202, 107401.

Deering D W. 1978. Rangeland reflectance characteristics measured by aircraft and spacecraftsensors. Ph D thesis, Texas A&M University, USA.

Demir N, Sönmez N K, Akar T, Ünal S. 2018. Automated measurement of plant height of wheat genotypes using a DSM derived from UAV imagery. Proceedings, 2, 350.

Feng X, Li Z, Yang P, Hong W, Wang A, Qin J, Zhang H, Kem Senou P D, Zhang Y, Wang D, Chen S. 2025. Enhance the accuracy of rice yield prediction through an advanced preprocessing architecture for time series data obtained from a UAV multispectral remote sensing platform. European Journal of Agronomy, 165, 127542.

Gao L, Wang X, Johnson B A, Tian Q, Wang Y, Verrelst J, Mu X, Gu X. 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 364-377.

Gitelson A A. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. Journal of Plant Physiology, 161, 165-173.

Gitelson A A, Kaufman Y J, Merzlyak M N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289-298.

Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Strachan I B. 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90, 337-352.

Haboudane D, Miller J R, Tremblay N, Zarco-Tejada P J, Dextraze L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, 416-426.

Hashimoto N, Saito Y, Yamamoto S, Ishibashi T, Ito R, Maki M, Homma K. 2022. Feasibility of yield estimation based on leaf area dynamics measurements in rice paddy fields of farmers. Field Crops Research, 286, 108609.

He J, Zhang X, Guo W, Pan Y, Yao X, Cheng T, Zhu Y, Cao W, Tian Y. 2020. Estimation of vertical leaf nitrogen distribution within a rice canopy based on hyperspectral data. Frontiers in Plant Science, 10, 1802.

Hirooka Y, Homma K, Shiraiwa T. 2018. Parameterization of the vertical distribution of leaf area index (LAI) in rice (Oryza sativa L.) using a plant canopy analyzer. Scientific Reports, 8, 6387.

Itakura K, Saito Y, Suzuki T, Kondo N, Hosoi F. 2019. Estimation of citrus maturity with fluorescence spectroscopy using deep learning. Horticulturae, 5, 2.

Jay S, Baret F, Dutartre D, Malatesta G, Héno S, Comar A, Weiss M, Maupas F. 2019. Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops. Remote Sensing of Environment, 231, 110898.

Jiang Z, Huete A R, Didan K, Miura T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833-3845.

Jiao L, Dong S, Zhang S, Xie C, Wang H. 2020. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Computers and Electronics in Agriculture, 174, 105522.

Jordan C F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50, 663-666.

Lei S, Luo J, Tao X, Qiu Z. 2021. Remote sensing detecting of yellow leaf disease of arecanut based on UAV multisource Sensors. Remote Sensing, 13, 4562.

Li K, Jiang C, Guan K, Wu G, Ma Z, Li Z. 2024. Evaluation of average leaf inclination angle quantified by indirect optical instruments in crop fields. International Journal of Applied Earth Observation and Geoinformation, 134, 104206.

Li W, Wang J, Zhang Y, Yin Q, Wang W, Zhou G, Huo Z. 2023. Combining texture, color, and vegetation index from unmanned aerial vehicle multispectral images to estimate winter wheat leaf area index during the vegetative growth stage. Remote Sensing, 15, 5715.

Li Y, Wang H, Dang L M, Sadeghi-Niaraki A, Moon H. 2020. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture, 169, 105174.

Li Z, Feng X, Li J, Wang D, Hong W, Qin J, Wang A, Ma H, Yao Q, Chen S. 2024. Time series field estimation of rice canopy height using an unmanned aerial vehicle-based RGB/multispectral platform. Agronomy, 14, 883.

Liao F, Feng X, Li Z, Wang D, Xu C, Chu G, Ma H, Yao Q, Chen S. 2024. A hybrid CNN-LSTM model for diagnosing rice nutrient levels at the rice panicle initiation stage. Journal of Integrative Agriculture, 23, 711-723.

Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. 2017. Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378-384.

Moslemi A. 2023. A tutorial-based survey on feature selection: Recent advancements on feature selection. Engineering Applications of Artificial Intelligence, 126, 107136.

Mulero G, Bonfil D J, Helman D. 2025. Wheat leaf area index retrieval from drone-derived hyperspectral and LiDAR imagery using machine learning algorithms. Agricultural and Forest Meteorology, 372, 110648.

Nie S, Wang C, Dong P, Xi X, Luo S, Zhou H. 2016. Estimating leaf area index of maize using airborne discrete-return LiDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 3259-3266.

Ninomiya S. 2022. High-throughput field crop phenotyping: Current status and challenges. Breeding Science, 72, 3-18.

Oehme L H, Reineke A J, Weiß T M, Würschum T, He X, Müller J. 2022. Remote sensing of maize plant height at different growth stages using UAV-based digital surface models (DSM). Agronomy, 12, 958.

Okada M, Barras C, Toda Y, Hamazaki K, Ohmori Y, Yamasaki Y, Takahashi H, Takanashi H, Tsuda M, Hirai M Y, Tsujimoto H, Kaga A, Nakazono M, Fujiwara T, Iwata H. 2024. High-throughput phenotyping of soybean biomass: Conventional trait estimation and novel latent feature extraction using UAV remote sensing and deep learning models. Plant Phenomics, 6, 0244.

Qiao L, Zhao R, Tang W, An L, Sun H, Li M, Wang N, Liu Y, Liu G. 2022. Estimating maize LAI by exploring deep features of vegetation index map from UAV multispectral images. Field Crops Research, 289, 108739.

Rondeaux G, Steven M, Baret F. 1996. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55, 95-107.

Schirrmann M, Hamdorf A, Giebel A, Dammer K H, Garz A. 2015. A mobile sensor for leaf area index estimation from canopy light transmittance in wheat crops. Biosystems Engineering, 140, 23-33.

Sripada R P, Heiniger R W, White J G, Meijer A D. 2006. Aerial color infrared photography for determining early in-sason nitrogen requirements in corn. Agronomy Journal, 98, 968-977.

Su X, Wang J, Ding L, Lu J, Zhang J, Yao X, Cheng T, Zhu Y, Cao W, Tian Y. 2023. Grain yield prediction using multi-temporal UAV-based multispectral vegetation indices and endmember abundance in rice. Field Crops Research, 299, 108992.

Sun Q, Sun L, Shu M, Gu X, Yang G, Zhou L. 2019. Monitoring maize lodging grades via unmanned aerial vehicle multispectral image. Plant Phenomics, 2019, 5704154.

Sun Y, Qin Q, Ren H, Zhang T, Chen S. 2020. Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery. IEEE Transactions on Geoscience and Remote Sensing, 58, 826-840.

Sun Y, Ren H, Zhang T, Zhang C, Qin Q. 2018. Crop leaf area index retrieval based on inverted difference vegetation index and NDVI. IEEE Geoscience and Remote Sensing Letters, 15, 1662-1666.

Sun Y, Wang B, Zhang Z. 2023. Improving leaf area index estimation with chlorophyll insensitive multispectral red-edge vegetation indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 3568-3582.

Thenmozhi K, Srinivasulu Reddy U. 2019. Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture, 164, 104906.

Wang F M, Huang J F, Tang Y L, Wang X Z. 2007. New vegetation index and its application in estimating leaf area index of rice. Rice Science, 14, 195-203.

Wang L, Chang Q, Li F, Yan L, Huang Y, Wang Q, Luo L. 2019. Effects of growth stage development on paddy rice leaf area index prediction models. Remote Sensing, 11, 361.

Wei B, Ma X, Guan H, Yu M, Yang C, He H, Wang F, Shen P. 2023. Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction. Ecological Informatics, 75, 102070.

Wen W, Wang J, Zhao Y, Wang C, Liu K, Chen B, Wang Y, Duan M, Guo X. 2024. 3D morphological feature quantification and analysis of corn leaves. Plant Phenomics, 6, 0225.

Wulder M A, LeDrew E F, Franklin S E, Lavigne M B. 1998. Aerial image texture information in the estimation of northern deciduous and mixed wood forest leaf area index (LAI). Remote Sensing of Environment, 64, 64-76.

Xie Q, Dash J, Huang W, Peng D, Qin Q, Mortimer H, Casa R, Pignatti S, Laneve G, Pascucci S, Dong Y, Ye H. 2018. Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 1482-1493.

Xie Q, Huang W, Zhang B, Chen P, Song X, Pascucci S, Pignatti S, Laneve G, Dong Y. 2016. Estimating winter wheat leaf area index from ground and hyperspectral observations using vegetation indices. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 771-780.

Yan K, Gao S, Yan G, Ma X, Chen X, Zhu P, Li J, Gao S, Gastellu-Etchegorry J P, Myneni R B, Wang Q. 2025. A global systematic review of the remote sensing vegetation indices. International Journal of Applied Earth Observation and Geoinformation, 139, 104560.

Yuan W, Meng Y, Li Y, Ji Z, Kong Q, Gao R, Su Z. 2023. Research on rice leaf area index estimation based on fusion of texture and spectral information. Computers and Electronics in Agriculture, 211, 108016.

Zang H, Wang Y, Yang X, He J, Zhou M, Zheng G, Li G. 2022. Estimation of density and height of winter wheat varieties using unmanned aerial vehicles images. Journal of Biobased Materials and Bioenergy, 16, 821-829.

Zarate-Valdez J L, Whiting M L, Lampinen B D, Metcalf S, Ustin S L, Brown P H. 2012. Prediction of leaf area index in almonds by vegetation indexes. Computers and Electronics in Agriculture, 85, 24-32.

Zhang J, Qiu X, Wu Y, Zhu Y, Cao Q, Liu X, Cao W. 2021. Combining texture, color, and vegetation indices from fixed-wing UAS imagery to estimate wheat growth parameters using multivariate regression methods. Computers and Electronics in Agriculture, 185, 106138.

Zhang Y, Ta N, Guo S, Chen Q, Zhao L, Li F, Chang Q. 2022. Combining spectral and textural information from UAV RGB images for leaf area index monitoring in kiwifruit orchard. Remote Sensing, 14, 1063.

Zheng W, Chen S, Fu Z, Zhu F, Yan H, Yang J. 2022. Feature selection boosted by unselected features. IEEE Transactions on Neural Networks and Learning Systems, 33, 4562-4574.

Zhu W, Sun Z, Huang Y, Lai J, Li J, Zhang J, Yang B, Li B, Li S, Zhu K, Li Y, Liao X. 2019. Improving field-scale wheat LAI retrieval based on UAV remote-sensing observations and optimized VI-LUTs. Remote Sensing, 11, 2456.

Zou X, Wang Q, Chen Y, Wang J, Xu S, Zhu Z, Yan C, Shan P, Wang S, Fu Y. 2025. Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy. Food Chemistry, 463, 141053.

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