UAV-based phenotyping identifies net assimilation rate as a diagnostic trait for synergistic enhancement of rice yield and grain quality

Abstract

Achieving rice yield-quality synergy, which is critical for breeding and agronomic practice, is hindered by dynamic regulatory gaps due to methodological constraints, while high-throughput unmanned aerial vehicle (UAV) phenotyping can enable breakthroughs by decoding dynamic traits at scale. This study conducted five experiments (EXP, 2022-2024; including nitrogen fertilization, multi-cultivar, and breeding material experiments) with UAV-based phenotyping to establish trait estimation models (EXP1-EXP3), enabling dissection of trait-specific contributions to yield-quality synergies via regression, multi-objective optimization, and path analysis (EXP4-EXP5), and identifying diagnostic traits in practice. Using UAV data, effective regression models were developed to monitor five rice traits: plant height (R² ​= ​0.89), aboveground biomass (R² ​= ​0.84), leaf area index (R² ​= ​0.61), canopy nitrogen content (R² ​= ​0.68), and leaf nitrogen content (R² ​= ​0.83), thereby systematically establishing 37 critical plant traits across the growth stages. Furthermore, feature importance analysis using extreme gradient boosting (R² ​= ​0.99) assessed the importance of these traits for yield and grain quality, and four common traits that were crucial for both yield and grain quality were identified. Notably, the synergistic yield-quality group exhibited 26.38-51.76% higher net assimilation rate (NAR) than the low-performance group (validated by multi-objective optimization), positioning NAR as a diagnostic marker for yield-quality synergistic enhancement. Path analysis revealed that NAR exerted positive effects on yield and grain quality, while yield indirectly influenced grain quality through eating quality. Overall, this study integrated UAV-based phenotyping and trait analysis, providing a novel insight into the synergistic enhancement of yield and grain quality.

Keywords

Grain quality / Net assimilation rate / Phenotyping / Rice / Unmanned aerial vehicle / Yield

Availability of data and materials

Not applicable.

Authors' contributions

W.W. and X.F.: writing original draft, investigation, and data curation; Z.L., J.Q., H.W., Y.Z., G.C., C.X., K.Y., and Y.L.: writing, reviewing, and editing; and D.W. and S.C.: writing, reviewing, editing, supervision, and conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was supported by the National Key Research and Deve-lopment Program of China “Research and demonstration of integrative approaches to synergistically improve yield, quality and efficiency in rice production in Southern China” (2022YFD2300700), Biological Breeding-National Science and Technology Major Project (2023ZD0407605), the Agricultural Science and Technology Innovation Program (ASTIP), the Central Public-Interest Scientific Institution Basal Research Fund, China (CPSIBRF-CNRRI-202410), the National Rice Industry Technology System of China (CARS-01), and the Open Project Program of the State Key Laboratory of Rice Biology and Breeding, China National Rice Research Institute (2023ZZKT20402). The authors wish to acknowledge the significant contribution of the Phenotyping Platform at the Public Laboratory of the China National Rice Research Institute for their technical support and the National Mid-term Genebank for Rice of China National Rice Research Institute for providing the rice germplasm collection.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.crope.2025.06.001.

References

[1] Alfatih, A., Wu, J., Zhang, Z., Xia, J., Jan, S.U., Yu, L., Xiang, C., 2020. Rice NIN-LIKE PROTEIN 1rapidly responds to nitrogen deficiency and improves yield and nitrogen use efficiency. J. Exp. Bot. 71, 6032-6042.

[2] Brede, B., Terryn, L., Barbier, N., Bartholomeus, H.M., Bartolo, R., Calders, K., Derroire, G., Moorthy, S.M.K., Lau, A., Levick, S.R., Raumonen, P., Verbeeck, H., Wang, D., Whiteside, T., van der Zee, J., Herold, M., 2022. Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning. Remote Sens. Environ. 280, 113180.

[3] Cao, S., Liu, B., Wang, D., Rasheed, A., Xie, L., Xia, X., He, Z., 2024. Orchestrating seed storage protein and starch accumulation toward overcoming yield-quality trade-off in cereal crops. J. Integr. Plant Biol. 66, 468-483.

[4] Chen, S., Tang, L., Sun, J., Xu, Q., Xu, Z., Chen, W., 2021. Contribution and prospect of erect panicle type to japonica super rice. Rice Sci. 28, 431-441.

[5] Chen, T., Guestrin, C., 2016. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, pp. 785-794.

[6] Cheng, Y, Xiao, F., Huang, D, Yang, Y., Cheng, W, Jin, S, Li, G, Ding, Y, Paul, M.J., Liu, Z, 2024. High canopy photosynthesis before anthesis explains the outstanding yield performance of rice cultivars with ideal plant architecture. Field Crops Res. 306, 109223.

[7] Chowdhury, M., Khura, T.K., Parray, R.A., Upadhyay, P.K., Kushwaha, H.L., Lama, A., Kushwah, A., 2025. Multi-vegetation indices based handheld device for precise nitrogen assessment and prescription in direct seeded rice. Comput. Electron. Agric. 229, 109886.

[8] Doan, Q.C., Chen, C., He, S., Zhang, X., 2024. How urban air quality affects land values: Exploring non-linear and threshold mechanism using explainable artificial intelligence. J. Clean Prod. 434, 140340.

[9] Duan, B., Fang, S., Gong, Y., Peng, Y., Wu, X., Zhu, R, 2021. Remote estimation of grain yield based on UAV data in different rice cultivars under contrasting climatic zone. Field Crops Res. 267, 108148.

[10] Fei, L., Yang, S., Ma, A., Lunzhu, C., Wang, M., Wang, G., Guo, S., 2023a. Grain chalkiness is reduced by coordinating the biosynthesis of protein and starch in fragrant rice (Oryza sativa L.) grain under nitrogen fertilization. Field Crops Res. 302, 109098.

[11] Fei, S., Hassan, M.A., Xiao, Y., Su, X., Chen, Z., Cheng, Q., Duan, F., Chen, R., Ma, Y., 2023b. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat. Precis. Agric. 24, 187-212.

[12] Gao, X., Yao, Y., Chen, S., Li, Q., Zhang, X., Liu, Z., Zeng, Y., Ma, Y., Zhao, Y., Li, S., 2024a. Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale. Eur. J. Agron. 161, 127360.

[13] Gao, Z., Yang, G., Zhao, X., Jiao, L., Wen, X., Liu, Y., Xia, X., Zhao, C., Dong, D., 2024b. Rapid measurement of anthocyanin content in grape and grape Juice: Raman spectroscopy provides non-destructive, rapid methods. Comput. Electron. Agric. 222, 109048.

[14] Guo, A., Ye, H., Huang, W., Qian, B., Wang, J., Lan, Y., Wang, S., 2023a. Inversion of maize leaf area index from UAV hyperspectral and multispectral imagery. Comput. Electron. Agric. 212, 108020.

[15] Guo, X., Wang, L., Zhu, G., Xu, Y., Meng, T., Zhang, W., Li, G., Zhou, G., 2023b. Impacts of inherent components and nitrogen fertilizer on eating and cooking quality of rice: a review. Foods 12, 2495.

[16] 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 Res. 286, 108609.

[17] Hobbie, J.G., Gandomi, A.H., Rahimi, I., 2021. A comparison of constraint handling techniques on NSGA-II. Arch. Comput. Methods Eng. 28, 3475-3490.

[18] Hong, W., Li, Z., Feng, X., Qin, J., Wang, A., Jin, S., Wang, D., Chen, S., 2024. Estimating key phenological dates of multiple rice accessions using unmanned aerial vehicle-based plant height dynamics for breeding. Rice Sci. 31, 617-628.

[19] Hou, M., Yu, M., Li, Z., Ai, Z., Chen, J., 2021. Molecular regulatory networks for improving nitrogen use efficiency in rice. Int. J. Mol. Sci. 22, 9040.

[20] Huang, M., Shu, A., Xie, J., Cao, F., Chen, J., Wang, W., Zheng, H., 2025. Nitrogen management for improving grain yield and quality in high-quality hybrid rice. J. Soil Sci. Plant Nutr. 25, 1480-1492.

[21] Huang, S., Tang, L., Hupy, J.P., Wang, Y., Shao, G., 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. J. For. Res. 32, 1-6.

[22] Huang, X., Yang, J., Zhou, W., Zhang, G., Liao, B., Wahab, A., Yi, Z., Tu, N., 2023. Comparison of the source-sink characteristics between main season and ratooning in rice (Oryza sativa L.). Agronomy 13, 1731.

[23] Islam, M.N., Masud, A.A.C., Alam, M.M., Islam, M.N., Rahman, M.L., Hasanuzzaman, M., 2022. Osmolyte-induced water deficit stress mitigation during panicle initiation stage in transplanted rice (Oryza sativa L.). Plant Sci. Today 9, 9-20.

[24] Ji, D., Xiao, W., Sun, Z., Liu, L., Gu, J., Zhang, H., Harrison, M.T., Liu, K., Wang, Z., Wang, W., Yang, J., 2023. Translocation and distribution of carbon-nitrogen in relation to rice yield and grain quality as affected by high temperature at early panicle initiation stage. Rice Sci. 30, 598-612.

[25] Jiang, H., Xu, X., Sun, A., Bai, C., Li, Y., Nuo, M., Shen, X., Li, W., Wang, D., Tian, P., Wei, X., Wang, G., Yang, M., Wu, Z., 2024a. Silicon nutrition improves the quality and yield of rice under dry cultivation. J. Sci. Food Agric. 104, 1897-1908.

[26] Jiang, Z., Chen, Q., Liu, D., Tao, W., Gao, S., Li, J., Lin, C., Zhu, M., Ding, Y., Li, W., Li, G., Sakr, S., Xue, L., 2024b. Application of slow-controlled release fertilizer coordinates the carbon flow in carbon-nitrogen metabolism to effect rice quality. BMC Plant Biol. 24, 621.

[27] Jin, K., Chen, G., Yang, Y., Zhang, Z., Lu, T., 2023. Strategies for manipulating Rubisco and creating photorespiratory bypass to boost C3 photosynthesis: Prospects on modern crop improvement. Plant Cell Environ. 46, 363-378.

[28] Jin, Z., Guo, S., Li, S., Yu, F., Xu, T., 2024. Research on the rice fertiliser decision-making method based on UAV remote sensing data assimilation. Comput. Electron. Agric. 216, 108508.

[29] Kchikich, A., Roussi, Z., Krid, A., Nhhala, N., Ennoury, A., Benmrid, B., Kounnoun, A., El Maadoudi, M., Nhiri, N., Mohamed, N., 2024. Effects of mycorrhizal symbiosis and Ulva lactuca seaweed extract on growth, carbon/nitrogen metabolism, and antioxidant response in cadmium-stressed sorghum plant. Physiol. Mol. Biol. Plants 30, 605-618.

[30] Konishi, N., Ishiyama, K., Matsuoka, K., Maru, I., Hayakawa, T., Yamaya, T., Kojima, S., 2014. NADH-dependent glutamate synthase plays a crucial role in assimilating ammonium in the Arabidopsis root. Physiol. Plant. 152, 138-151.

[31] Li, D., Yang, S., Du, Z., Xu, X., Zhang, P., Yu, K., Zhang, J., Shu, M., 2024. Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review. Comput. Electron. Agric. 227, 109565.

[32] Li, G., Li, W., Liang, Y., Lu, W., Lu, D., 2023a. Spraying exogenous hormones alleviate impact of weak-light on yield by improving leaf carbon and nitrogen metabolism in fresh waxy maize. Front. Plant Sci. 14, 1220827.

[33] Li, J., Chen, G., Zhang, J., Shen, H., Kang, J., Feng, P., Xie, Q., Hu, Z., 2020. Suppression of a hexokinase gene, SlHXK1, leads to accelerated leaf senescence and stunted plant growth in tomato. Plant Sci. 298, 110544.

[34] Li, J., Feng, Y., Wang, X., Xu, G., Luo, Z., Peng, J., Luo, Q., Lu, W., Han, Z., 2022. High nitrogen input increases the total spikelets but decreases the high-density grain content in hybrid indica rice. Field Crops Res. 288, 108679.

[35] Li, J., Li, G., Wang, L., Li, D., Li, H., Gao, C., Zhuang, M., Zhuang, J., Zhou, H., Xu, S., Hu, Z., Wang, E., 2023b. Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning. Field Crops Res. 302, 109102.

[36] Liu, Q., Li, M., Ji, X., Liu, J., Wang, F., Wei, Y., 2022. Characteristics of grain yield, dry matter production and nitrogen uptake and transport of rice varieties with different grain protein content. Agronomy 12, 2866.

[37] Liu, S., Hu, G., Du, S., Gao, H., Chang, L., 2025. A new evidential reasoning rule considering evidence correlation with maximum information coefficient and application in fault diagnosis. Sensors 25, 3111.

[38] Ma, Z., Cao, J., Chen, X., Yu, J., Liu, G., Xu, F., Hu, Q., Li, G., Zhu, Y., Zhang, H., Wei, H., 2025. Differences in carbon and nitrogen metabolism of soft japonica rice in southern China during grain filling stage under different light and nitrogen fertilizer conditions and their relationship with rice eating quality. Front. Plant Sci. 16, 1534625.

[39] Nie, Y., Sa, R., Chumachenko, S., Hu, Y., Wang, Y., Fan, W., 2024. Inversion of forest aboveground biomass in regions with complex terrain based on PolSAR data and a machine learning model: radiometric terrain correction assessment. Remote Sens. 16, 2229.

[40] Niu, Y., Chen, T., Zhao, C., Zhou, M., 2022. Lodging prevention in cereals: Morphological, biochemical, anatomical traits and their molecular mechanisms, management and breeding strategies. Field Crops Res. 289, 108733.

[41] Pan, L., Song, C., Gan, X., Xu, K., Xie, Y., 2024. Military image captioning for low-altitude UAV or UGV perspectives. Drones 8, 421.

[42] Qi, X., Song, J., Qi, H., Shi, Y., 2024. Maximum information coefficient feature selection method for interval-valued data. IEEE Access 12, 53752-53766.

[43] Shao, R., Jia, S., Tang, Y., Zhang, J., Li, H., Li, L., Chen, J., Guo, J., Wang, H., Yang, Q., Wang, Y., Liu, T., Zhao, X., 2021. Soil water deficit suppresses development of maize ear by altering metabolism and photosynthesis. Environ. Exp. Bot. 192, 104651.

[44] Shi, Y., Guo, E., Cheng, X., Wang, L., Jiang, S., Yang, X., Ma, H., Zhang, T., Li, T., Yang, X., 2022. Effects of chilling at different growth stages on rice photosynthesis, plant growth, and yield. Environ. Exp. Bot. 203, 105045.

[45] Song, X., Meng, X., Guo, H., Cheng, Q., Jing, Y., Chen, M., Liu, G., Wang, B., Wang, Y., Li, J., Yu, H., 2022. Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol. 40, 1403-1411.

[46] Stovall, A.E.L., Vorster, A.G., Anderson, R.S., Evangelista, P.H., Shugart, H.H., 2017. Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sens. Environ. 200, 31-42.

[47] 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.

[48] Sun, T., Liu, B., Hasegawa, T., Liao, Z., Tang, L., Liu, L., Cao, W., Zhu, Y., 2023. Sink-source unbalance leads to abnormal partitioning of biomass and nitrogen in rice under extreme heat stress: An experimental and modeling study. Eur. J. Agron. 142, 126678.

[49] Wang, B., Gu, S., Wang, J., Chen, B., Wen, W., Guo, X., Zhao, C., 2024. Maximizing the radiation use efficiency by matching the leaf area and leaf nitrogen vertical distributions in a maize canopy: a simulation study. Plant Phenomics 6, 0217.

[50] Wang, Y., 2024. Improving photosynthetic efficiency in fluctuating light to enhance yield of C3 and C4 crops. Crop Environ. 3, 184-193.

[51] Wang, Y., An, J., Shao, M., Wu, J., Zhou, D., Yao, X., Zhang, X., Cao, W., Jiang, C., Zhu, Y., 2025. A comprehensive review of proximal spectral sensing devices and diagnostic equipment for field crop growth monitoring. Precis. Agric. 26, 54.

[52] Wang, Z., Jia, Y., Fu, J., Qu, Z., Wang, X., Zou, D., Wang, J., Liu, H., Zheng, H., Wang, J., Yang, L., Xu, H., Zhao, H., 2022. An analysis based on japonica rice root characteristics and crop growth under the interaction of irrigation and nitrogen methods. Front. Plant Sci. 13, 890983.

[53] Wei, H., Ge, J., Zhang, X., Zhu, W., Chen, Y., Meng, T., Dai, Q., 2022. Agronomic and physicochemical properties facilitating the synchronization of grain yield and the overall palatability of japonica rice in East China. Agriculture 12, 969.

[54] Wu, G., Chen, X., Zang, Y., Ye, Y., Qian, X., Zhang, W., Zhang, H., Liu, L., Zhang, Z., Wang, Z., Gu, J., Yang, J., 2024. An optimized strategy of nitrogen-split application based on the leaf positional differences in chlorophyll meter readings. J. Integr. Agric. 23, 2605-2617.

[55] Xie, C., Yang, C., 2020. A review on plant high-throughput phenotyping traits using UAV- based sensors. Comput. Electron. Agric. 178, 105731.

[56] Xu, W., Liu, S., Feng, J., Wang, B., Shao, Z., Wang, Y., Hou, W., Gao, Q., 2023. Rice canopy light resources allocation, leaf net photosynthetic rate, and yield formation characteristics response to combined application of nitrogen and potassium. J. Soil Sci. Plant Nutr. 23, 5257-5269.

[57] Xu, Y., Ma, K., Zhao, Y., Wang, X., Zhou, K., Yu, G., Li, C., Li, P., Yang, Z., Xu, C., Xu, S., 2021. Genomic selection: A breakthrough technology in rice breeding. Crop J. 9, 669-677.

[58] Yang, D., Luo, Y., Kong, X., Huang, C., Wang, Z., 2021. Interactions between exogenous cytokinin and nitrogen application regulate tiller bud growth via sucrose and nitrogen allocation in winter wheat. J. Plant Growth Regul. 40, 329-341.

[59] Yang, K., Mo, J., Luo, S., Peng, Y., Fang, S., Wu, X., Zhu, R., Li, Y., Yuan, N., Zhou, C., Gong, Y., 2023. Estimation of rice aboveground biomass by UAV imagery with photosynthetic accumulation models. Plant Phenomics 5, 0056.

[60] Yoneyama, T., Tanno, F., Tatsumi, J., Mae, T., 2016. Whole-plant dynamic system of nitrogen use for vegetative growth and grain filling in rice plants (Oryza sativa L.) as revealed through the production of 350 grains from a germinated seed over 150 days: a review and synthesis. Front. Plant Sci. 7, 01151.

[61] Yu, G., Xie, Z., Lei, S., Li, H., Xu, B., Huang, B., 2022. The NAC factor LpNAL delays leaf senescence by repressing two chlorophyll catabolic genes in perennial ryegrass. Plant Physiol. 189, 595-610.

[62] Yue, J., Yang, H., Yang, G., Fu, Y., Wang, H., Zhou, C., 2023. Estimating vertically growing crop above-ground biomass based on UAV remote sensing. Comput. Electron. Agric. 205, 107627.

[63] Zhang, G., Sakai, H., Tokida, T., Usui, Y., Zhu, C., Nakamura, H., Yoshimoto, M., Fukuoka, M., Kobayashi, K., Hasegawa, T., 2013. The effects of free-air CO2 enrichment (FACE) on carbon and nitrogen accumulation in grains of rice (Oryza sativa L.). J. Exp. Bot. 64, 3179-3188.

[64] Zhang, J., Cheng, T., Shi, L., Wang, W., Niu, Z., Guo, W., Ma, X., 2022. Combining spectral and texture features of UAV hyperspectral images for leaf nitrogen content monitoring in winter wheat. Int. J. Remote Sens. 43, 2335-2356.

[65] Zhao, Z., Wang, C., Yu, X., Tian, Y., Wang, W., Zhang, Y., Bai, W., Yang, N., Zhang, T., Zheng, H., Wang, Q., Lu, J., Lei, D., He, X., Chen, K., Gao, J., Liu, X., Liu, S., Jiang, L., Wang, H., Wan, J., 2022. Auxin regulates source-sink carbohydrate partitioning and reproductive organ development in rice. Proc. Natl. Acad. Sci. U. S. A. 119, e2121671119.

[66] Zheng, X., Wei, F., Cheng, C., Qian, Q., 2024. A historical review of hybrid rice breeding. J. Integr. Plant Biol. 66, 532-545.

[67] Zhu, Y., Huang, J., Wu, T., Ren, X., 2021. Identification method of cashmere and wool based on texture features of GLCM and Gabor. J. Eng. Fiber Fabr. 16, 1558925021989179.