Identification of the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data

doi:10.1016/j.jia.2025.02.026

Journal of Integrative Agriculture

2026, Vol. 25

Issue (6): 2396-2413 DOI: 10.1016/j.jia.2025.02.026

Crop Science

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Identification of the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data

Qin Dai, Hong Chen, Ziqiang Chen, Chang Liu, Gaoliang Li, Yakun Wang^#, Xiaotao Hu^#

Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education/Northwest A&F University, Yangling 712100, China

Highlights

● A structured framework to identify the optimal phenological periods for summer maize yield prediction using unmanned aerial vehicle (UAV)-based multispectral data was proposed.

● The tasseling stage is the earliest suitable period for maize yield prediction.

● Integrating data from the tasseling, silking, and dough stages is better than either mono-temporal or full-temporal data.

Abstract
References

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摘要

及时准确地预测作物产量对粮食管理和贸易至关重要。然而，很少有研究探讨不同物候期的作物表型参数（CPPs）与无人机（UAV）数据的结合对玉米产量预测的影响。与单时相数据相比，多时相数据能够在多大程度上提高产量预测的准确性和可靠性，这一点也尚未得到系统的研究。为了实现作物产量估算精度与监测成本之间的平衡，本研究提出了一种结构化框架，旨在识别利用无人机多光谱数据进行夏玉米产量预测的最佳物候期。本研究首先采用了三种经典方法，即自定义平均精度减少法（C-MDA）、基于最优参数的地理探测器（OPGD）和灰色关联分析（GRA），对六个生长阶段中的CPPs和基于无人机信息计算的植被指数（VI）进行排序和筛选。进而分别建立了基于单时相数据和多时相数据组合的岭回归模型，并对其在产量预测中的表现进行比较，以确定最佳物候期和相应的关键因素。研究结果表明，与OPGD和GRA相比，C-MDA在因子筛选排名方面表现出明显的优势。绿色归一化差异植被指数（GNDVI）、归一化差异植被系数（NDVI）和归一化差异红边指数（NDRE）表现最佳，而叶面积指数（LAI）和地上生物量（AGB）被证明是最有效的CPPs。当仅使用单时相数据进行产量预测时，腊熟期的预测精度最高（R2=0.871，RMSE=0.407 t ha^-1），而抽雄期则能够最早实现精度尚可的产量估算（R2=0.810，RMSE=0.493 t ha^-1）。相比之下，整合来自不同作物生长阶段的无人机数据显著提高了产量估算的准确性。研究建议采用抽雄期、吐丝期和腊熟期的数据组合（R2=0.942，RMSE=0.291 t ha-1）。以上发现表明，在小农户田块中进行精确的玉米产量估算是可行的，研究为精准农业的发展提供了重要的理论依据和实践指导。

Abstract

Timely and accurate forecasting of crop yields is critical for food management and trade. However, only limited research has explored the impact of integrating crop phenotypic parameters (CPPs) with unmanned aerial vehicle (UAV) data across different phenological stages on maize yield prediction. The extent to which multi-temporal data enhances the accuracy and reliability of yield projections compared to mono-temporal data has yet to be systematically investigated. To attain the proper balance between accuracy and cost in crop yield estimation, this study proposed a structured framework for identifying the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data. Three classical methods of custom mean decrease accuracy (C-MDA), optimal parameters-based geographical detector (OPGD), and grey relational analysis (GRA) were first used to sort and screen both the CPPs and vegetation indices (VIs) derived from UAV-based information over six growth stages. Ridge regression models based on multi-temporal data combinations and mono-temporal data were established separately, and their performance in yield prediction were compared to identify the optimal phenological stages and the corresponding key factors. Our results showed that C-MDA was much better at factor screening and ranking compared to OPGD and GRA. The green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and normalized difference red edge index (NDRE) emerged as the top-performing VIs, while the leaf area index (LAI) and above ground biomass (AGB) proved to be the most effective CPPs. When predicting yield using only mono-temporal data, the dough stage delivered the highest predictive accuracy (R²=0.871, RMSE=0.407 t ha^–1), while the tasseling stage was the earliest that achieved yield estimates with acceptable precision (R²=0.810, RMSE=0.493 t ha^–1). In contrast, the integration of UAV data from different crop growth stages markedly enhanced the accuracy of yield estimation. Combinations of data from the tasseling, silking, and dough stages were recommended as the best option (R²=0.942, RMSE=0.291 t ha^–1). These findings indicate that the precise estimation of maize yields in smallholder fields may be attainable, and present both substantial theoretical insights and practical benefits for the advancement of precision agriculture.

Keywords: UAV yield estimation multispectral data geographical detector

Received: 25 September 2024 Accepted: 24 January 2025 Online: 18 February 2025

Fund:

The study was funded by the National Natural Science Foundation of China (U2243235 and 52309060).

About author: #Correspondence Yakun Wang, E-mail: wangyakun@nwafu.edu.cn; Xiaotao Hu, E-mail: huxiaotao11@nwsuaf.edu.cn

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Qin Dai, Hong Chen, Ziqiang Chen, Chang Liu, Gaoliang Li, Yakun Wang, Xiaotao Hu. 2026. Identification of the optimal phenological periods for summer maize yield prediction using UAV-based multispectral data. Journal of Integrative Agriculture, 25(6): 2396-2413.

Ablikim K, Yang H, Mamattursun A. 2023. Spatiotemporal variation of evapotranspiration and its driving factors in the Urumqi River Basin. Sustainability, 15, 13904.

Bénard C, Da Veiga S, Scornet E. 2022. Mean decrease accuracy for random forests: Inconsistency, and a practical solution via the Sobol-MDA. Biometrika, 109, 881–900.

Bian C, Shi H, Wu S, Zhang K, Wei M, Zhao Y, Sun Y, Zhuang H, Zhang X, Chen S. 2022. Prediction of field-scale wheat yield using machine learning method and multi-spectral UAV data. Remote Sensing, 14, 1474.

Breiman L. 2001. Random forests. Machine Learning, 45, 5–32.

Cen H, Wan L, Zhu J, Li Y, Li X, Zhu Y, Weng H, Wu W, Yin W, Xu C, Bao Y, Feng L, Shou J, He Y. 2019. Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras. Plant Methods, 15, 32.

Chen J M. 1996. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22, 229–242.

Dar I S, Chand S, Shabbir M, Kibria B M G. 2023. Condition-index based new ridge regression estimator for linear regression model with multicollinearity. Kuwait Journal of Science, 50, 91–96.

Dee J R, Adams H D, Palmer M W. 2018. Belowground annual ring growth coordinates with aboveground phenology and timing of carbon storage in two tallgrass prairie forb species. American Journal of Botany, 105, 1975–1985.

Devkota K P, Bouasria A, Devkota M, Nangia V. 2024. Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco. European Journal of Agronomy, 158, 127195.

Duan B, Fang S, Zhu R, Wu X, Wang S, Gong Y, Peng Y. 2019. Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Frontiers in Plant Science, 10, 00204.

Fu T, Tian S, Zhan Q. 2024. Phenological analysis and yield estimation of rice based on multi-spectral and SAR data in Maha Sarakham, Thailand. Journal of Spatial Science, 69, 149–165.

García-Martínez H, Flores-Magdaleno H, Ascencio-Hernández R, Khalil-Gardezi A, Tijerina-Chávez L, Mancilla-Villa O R, Vázquez-Peña M A. 2020. Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicles. Agriculture, 10, 277.

Gilliot J M, Michelin J, Hadjard D, Houot S. 2021. An accurate method for predicting spatial variability of maize yield from UAV-based plant height estimation: A tool for monitoring agronomic field experiments. Precision Agriculture, 22, 897–921.

Gitelson A A, Merzlyak M N, Grits Y. 1997. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18, 2691–2697.

Goel N S, Qin W. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation. Remote Sensing Reviews, 10, 309–347.

Gong Y, Duan B, Fang S, Zhu R, Wu X, Ma Y, Peng Y. 2018. Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis. Plant Methods, 14, 70.

Gu Z, Chen X, Ruan W, Zheng M, Gen K, Li X, Deng H, Chen Y, Liu M. 2024. Quantifying the direct and indirect effects of terrain, climate and human activity on the spatial pattern of kNDVI-based vegetation growth: A case study from the Minjiang River Basin, Southeast China. Ecological Informatics, 80, 102493.

Guan K, Wu J, Kimball J S, Anderson M C, Frolking S, Li B, Hain C R, Lobell D B. 2017. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sensing of Environment, 199, 333–349.

Hoerl A E, Kennard R. 1970. Ridge regression: Applications to nonorthogonal problems. Technometrics, 12, 69–82.

Horie T, Yajima M, Nakagawa H. 1992. Yield forecasting. Agricultural Systems, 40, 211–236.

Huang J, Sedano F, Huang Y, Ma H, Li X, Liang S, Tian L, Zhang X, Fan J, Wu W. 2016. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188–202.

Ikram M, Zhang Q, Sroufe R, Shah S Z A. 2020. Towards a sustainable environment: The nexus between ISO 14001, renewable energy consumption, access to electricity, agriculture and CO2 emissions in SAARC countries. Sustainable Production and Consumption, 22, 218–230.

Ji Y, Liu R, Xiao Y, Cui Y, Chen Z, Zong X, Yang T. 2023. Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning. Precision Agriculture, 24, 1439–1460.

John K, Reddy P R. 2019. Correlation and path analysis for yield and yield attributes in groundnut (Arachis hypogaea L.). Legume Research, 42, 518–522.

Kearns M, Ron D. 1999. Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Computation, 11, 1427–1453.

Khaki S, Wang L. 2019. Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621.

Khanal S, Fulton J, Klopfenstein A, Douridas N, Shearer S. 2018. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Computers and Electronics in Agriculture, 153, 213–225.

Killeen P, Kiringa I, Yeap T, Branco P. 2024. Corn grain yield prediction using UAV-based high spatiotemporal resolution imagery, machine learning, and spatial cross-validation. Remote Sensing, 16, 683.

Knoben W J M, Freer J E, Woods R A. 2019. Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrology and Earth System Sciences, 23, 4323–4331.

Kumar C, Mubvumba P, Huang Y, Dhillon J, Reddy K. 2023. Multi-stage corn yield prediction using high-resolution UAV multispectral data and machine learning models. Agronomy, 13, 1277.

Lecerf R, Ceglar A, López-Lozano R, Van Der Velde M, Baruth B. 2019. Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe. Agricultural Systems, 168, 191–202.

Li G, Zhang J, Yang C, Song Y, Zheng C, Liu Z, Wang S, Tang S, Ding Y. 2014. Yield and yield components of hybrid rice as influenced by nitrogen fertilization at different eco-sites. Journal of Plant Nutrition, 37, 244–258.

Li L, Liu Z, Du X J. 2021. Improvement of analytic hierarchy process based on grey correlation model and its engineering application. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems (Part A: Civil Engineering), 7, 04021007.

Liu S B, Jin X L, Bai Y, Wu W B, Cui N B, Cheng M H, Liu Y D, Meng L, Jia X, Nie C W, Yin D M. 2023. UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background. International Journal of Applied Earth Observation and Geoinformation, 121, 103383.

Liu Y, Sun L, Liu B, Wu Y, Ma J, Zhang W, Wang B, Chen Z. 2023. Estimation of winter wheat yield using multiple temporal vegetation indices derived from UAV-based multispectral and hyperspectral imagery. Remote Sensing, 15, 4800.

Ma Y, Ma L, Zhang Q, Huang C, Yi X, Chen X, Hou T, Lv X, Zhang Z. 2022. Cotton yield estimation based on vegetation indices and texture features derived from RGB image. Frontiers in Plant Science, 13, 925986.

Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi F B. 2020. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599.

Nada H, Siniša J, Anto M, Vladimir M, Dragana M. 2016. Correlation and path analysis of yield and yield components of confectionary sunflower. Genetika, 48, 827–835.

Paudel D, de Wit A, Boogaard H, Marcos D, Osinga S, Athanasiadis I N. 2023. Interpretability of deep learning models for crop yield forecasting. Computers and Electronics in Agriculture, 206, 107663.

Peng G, Ruiliang P, Biging G S, Larrieu M R. 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41, 1355–1362.

Peng X, Han W, Ao J, Wang Y. 2021. Assimilation of LAI derived from UAV multispectral data into the SAFY model to estimate maize yield. Remote Sensing, 13, 1094.

Pokhrel A, Virk S, Snider J L, Vellidis G, Hand L C, Sintim H Y, Parkash V, Chalise D P, Lee J M, Byers C. 2023. Estimating yield-contributing physiological parameters of cotton using UAV-based imagery. Frontiers in Plant Science, 14, 1248152.

Qader S H, Dash J, Atkinson P M. 2018. Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Science of the Total Environment, 613, 250–262.

Qi J, Chehbouni A, Huete A R, Kerr Y H, Sorooshian S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126.

Qiao Y, Wang X, Han Z, Tian M, Wang Q, Wu H, Liu F. 2022. Geodetector based identification of influencing factors on spatial distribution patterns of heavy metals in soil: A case in the upper reaches of the Yangtze River, China. Applied Geochemistry, 146, 105459.

Richetti J, Lawes R A, Whan A, Gaydon D S, Thorburn P J. 2024. How well does APSIM NextGen simulate wheat yields across Australia using gridded input data? Validating continental-scale crop model simulations. European Journal of Agronomy, 158, 127212.

Ramos A P M, Osco L P, Furuya D E G, Gonçalves W N, Santana D C, Teodoro L P R, Silva Junior C A D, Capristo-Silva G F, Li J, Baio F H R, Marcato Junior J, Teodoro P E, Pistori H. 2020. A random forest ranking approach to predict yield in maize with UAV-based vegetation spectral indices. Computers and Electronics in Agriculture, 178, 105791.

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

Rouse J W, Haas R H, Schell J A, Deering D W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309–317.

Samberg L H, Gerber J S, Ramankutty N, Herrero M, West P C. 2016. Subnational distribution of average farm size and smallholder contributions to global food production. Environmental Research Letters, 11, 124010.

Sapkota S, Paudyal D R. 2023. Growth monitoring and yield estimation of maize plant using unmanned aerial vehicle (UAV) in a hilly region. Sensors, 23, 5432.

Schuerger A C, Capelle G A, Di Benedetto J A, Mao C, Thai C N, Evans M D, Richards J T, Blank T A, Stryjewski E C. 2003. Comparison of two hyperspectral imaging and two laser-induced fluorescence instruments for the detection of zinc stress and chlorophyll concentration in bahia grass (Paspalum notatum Flugge.). Remote Sensing of Environment, 84, 572–588.

Schut A G T, Traore P C S, Blaes X, de By R A. 2018. Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Research, 221, 98–107.

Sharma V, Honkavaara E, Hayden M, Kant S. 2024. UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning. Plant Stress, 12, 100464.

Song Y, Wang J, Ge Y, Xu C. 2020. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GIScience & Remote Sensing, 57, 593–610.

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 J, Lai Z, Di L, Sun Z, Tao J, Shen Y. 2020. Multilevel deep learning network for county-level corn yield estimation in the U.S. corn belt. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5048–5060.

Széles A V, Megyes A, Nagy J. 2012. Irrigation and nitrogen effects on the leaf chlorophyll content and grain yield of maize in different crop years. Agricultural Water Management, 107, 133–144.

Taylor K E. 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research (Atmospheres), 106, 7183–7192.

Uno Y, Prasher S O, Lacroix R, Goel P K, Karimi Y, Viau A, Patel R M. 2005. Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Computers and Electronics in Agriculture, 47, 149–161.

de Villiers C, Mashaba-Munghemezulu Z, Munghemezulu C, Chirima G J, Tesfamichael S G. 2024. Assessing maize yield spatiotemporal variability using unmanned aerial vehicles and machine learning. Geomatics, 4, 213–236.

Voitik A, Kravchenko V, Pushka O, Kutkovetska T, Shchur T, Kocira S. 2023. Comparison of NDVI, NDRE, MSAVI and NDSI indices for early diagnosis of crop problems. Agricultural Engineering, 27, 47–57.

Wan L, Cen H, Zhu J, Zhang J, Zhu Y, Sun D, Du X, Zhai L, Weng H, Li Y, Li X, Bao Y, Shou J, He Y. 2020. Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer - A case study of small farmlands in the South of China. Agricultural and Forest Meteorology, 291, 108096.

Wanthanaporn U, Supit I, Chaowiwat W, Hutjes R W A. 2024. Skill of rice yields forecasting over Mainland Southeast Asia using the ECMWF SEAS5 ensemble prediction system and the WOFOST crop model. Agricultural and Forest Meteorology, 351, 110001.

Yang B, Zhu W, Rezaei E E, Li J, Sun Z, Zhang J J R S. 2022. The optimal phenological phase of maize for yield prediction with high-frequency UAV remote sensing. Remote Sensing, 14, 1559.

Yang G, Li Y, Yuan S, Zhou C, Xiang H, Zhao Z, Wei Q, Chen Q, Peng S, Xu L. 2024. Enhancing direct-seeded rice yield prediction using UAV-derived features acquired during the reproductive phase. Precision Agriculture, 25, 1014–1037.

Yang L, Ji X, Li M, Yang P, Jiang W, Chen L, Yang C, Sun C, Li Y. 2024. A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector–machine learning coupled model. Geoscience Frontiers, 15, 101889.

Yu X, Zhang Q, Gao J, Wang Z, Borjigin Q, Hu S, Zhang B, Ma D. 2019. Planting density tolerance of high-yielding maize and the mechanisms underlying yield improvement with subsoiling and increased planting density. Agronomy, 9, 370.

Yuan N, Gong Y, Fang S, Liu Y, Duan B, Yang K, Wu X, Zhu R. 2021. UAV remote sensing estimation of rice yield based on adaptive spectral endmembers and bilinear mixing model. Remote Sensing, 13, 2190.

Yue J, Feng H, Yang G, Li Z. 2018. A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sensing, 10, 66.

Zhai L, Zhang L, Cui Y, Zhai L, Zheng M, Yao Y, Zhang J, Hou W, Wu L, Jia X. 2024. Combined application of organic fertilizer and chemical fertilizer alleviates the kernel position effect in summer maize by promoting post-silking nitrogen uptake and dry matter accumulation. Journal of Integrative Agriculture, 23, 1179–1194.

Zhang B, Gu L, Dai M, Bao X, Sun Q, Qu X, Zhang M, Liu X, Fan C, Gu X H, Zhen W H. 2024. Estimation of grain filling rate and thousand-grain weight of winter wheat (Triticum aestivum L.) using UAV-based multispectral images. European Journal of Agronomy, 159, 127258.

Zhang M, Kafy A A, Ren B, Zhang Y, Tan S, Li J. 2022. Application of the optimal parameter geographic detector model in the identification of influencing factors of ecological quality in Guangzhou, China. Land, 11, 1303.

Zhang P, Lu B, Shang J, Wang X, Hou Z, Jin S, Yang Y, Zang H, Ge J, Zeng Z. 2024. Ensemble learning for oat yield prediction using multi-growth stage UAV images. Remote Sensing, 16, 4575.

Zhou X, Zheng H B, Xu X Q, He J Y, Ge X K, Yao X, Cheng T, Zhu Y, Cao W X, Tian Y C. 2017. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246–255.

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