Abstract
Aims
Combining multiple features of UAV-based multispectral images with the stacking ensemble model, to improve the feasibility and accuracy of evaluating water stress in winter wheat.
Methods
UAV-based multispectral images of winter wheat with different moisture treatments were acquired, from which the features such as spectrum, texture, and color moments were extracted. The soil moisture content (SMC) as well as fuel moisture content (FMC), plant moisture content (PMC), and above-ground biomass (AGB) were collected for charging the degree of water stress. The basic models were used to build ensemble models such as stacking and weighted stacking (WE-stacking), and we estimated SMC, FMC, PMC and AGB combined with multiple features. The performance of these models was evaluated.
Results
The more severe the water stress, the lower values of SMC, FMC, PMC and AGB were obtained with estimation models. The performance of estimation models based on multi-feature fusion outperformed single feature in the evaluation of winter-wheat water stress. In the estimation of SMC, both stacking and WE-stacking models performed better than the basic models. Compared to the stacking model, the WE-stacking model had higher accuracy, with R2 increased between 1.98% and 3.62% at different soil depths. The WE-stacking model with multi-feature fusion still had sufficient stability and high accuracy in FMC, PMC and AGB estimation, with R2 of 0.866, 0.881 and 0.884, respectively.
Conclusions
The multi-feature fusion of UAV multispectral images combined with WE-stacking model has great application potential and provides technical support in evaluating crop water stress.
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Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Ahmad U, Alvino A, Marino S (2021) A review of crop water stress assessment using remote sensing. Remote Sensing. 13(20):4155. https://doi.org/10.3390/rs13204155
Ali N, Akmal M (2022) Wheat growth, yield, and quality under water deficit and reduced nitrogen supply. a review. Gesunde Pflanzen. 74(2):371–383. https://doi.org/10.1007/s10343-021-00615-w
Araujo JMM, Peixoto ZMA (2019) A new proposal for automatic identification of multiple soybean diseases. Comput Electron Agric 167:105060. https://doi.org/10.1016/j.compag.2019.105060
Babaeian E, Paheding S, Siddique N, Devabhaktuni VK, Tuller M (2021) Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning. Remote Sensing of Environment. 260:112434. https://doi.org/10.1016/j.rse.2021.112434
Bai X, Chen Y, Chen J, Cui W, Tai X, Zhang Z, Cui J, Ning J (2021) Optimal window size selection for spectral information extraction of sampling points from UAV multispectral images for soil moisture content inversion. Comput Electron Agric 190:106456. https://doi.org/10.1016/j.compag.2021.106456
Behmann J, Mahlein A-K, Rumpf T, Römer C, Plümer L (2014) A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis Agric 16(3):239–260. https://doi.org/10.1007/s11119-014-9372-7
Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G (2015) Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf 39:79–87. https://doi.org/10.1016/j.jag.2015.02.012
Bertalan L, Holb I, Pataki A, Négyesi G, Szabó G, Kupásné Szalóki A, Szabó S (2022) UAV-based multispectral and thermal cameras to predict soil water content – A machine learning approach. Comput Electron Agric 200:107262. https://doi.org/10.1016/j.compag.2022.107262
Breiman L (1996) Stacked regressions. Mach Learn 24(1):49–64. https://doi.org/10.1007/BF00117832
Cao Z, Yao X, Liu H, Liu B, Cheng T, Tian Y, Cao W, Zhu Y (2019) Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat. Agric For Meteorol 265:121–136. https://doi.org/10.1016/j.agrformet.2018.11.009
Carpintero E, Mateos L, Andreu A, González-Dugo M (2020) Effect of the differences in spectral response of mediterranean tree canopies on the estimation of evapotranspiration using vegetation index-based crop coefficients. Agric Water Manag 238:106201. https://doi.org/10.1016/j.agwat.2020.106201
Champion I, Dubois-Fernandez P, Guyon D, Cottrel M (2008) Radar image texture as a function of forest stand age. Int J Remote Sens 29(6):1795–1800. https://doi.org/10.1080/01431160701730128
Chaves MM, Oliveira MM (2004) Mechanisms underlying plant resilience to water deficits: prospects for water-saving agriculture. J Exp Bot 55(407):2365–2384. https://doi.org/10.1093/jxb/erh269
Cheng M, Jiao X, Liu Y, Shao M, Yu X, Bai Y, Wang Z, Wang S, Tuohuti N, Liu S, Shi L, Yin D, Huang X, Nie C, Jin X (2022) Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agric. Water Manag 264:107530. https://doi.org/10.1016/j.agwat.2022.107530
Cheng Q, Xu H, Fei S, Li Z, Chen Z (2022) Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments. Agriculture. 12:1267. https://doi.org/10.3390/agriculture12081267
Condran S, Bewong M, Islam MZ, Maphosa L, Zheng L (2022) Machine learning in precision agriculture: A survey on trends, applications and evaluations over two decades. IEEE Access. 10:73786–73803. https://doi.org/10.1109/access.2022.3188649
Djanaguiraman M, Prasad PVV, Kumari J, Rengel Z (2018) Root length and root lipid composition contribute to drought tolerance of winter and spring wheat. Plant Soil. 439(1–2):57–73. https://doi.org/10.1007/s11104-018-3794-3
Fang Y, Du Y, Wang J, Wu A, Qiao S, Xu B, Zhang S, Siddique K, Chen Y (2017) Moderate drought stress affected root growth and grain yield in old, modern and newly released cultivars of winter wheat. Front Plant Sci 8:672. https://doi.org/10.3389/fpls.2017.00672
Feilhauer H, Asner GP, Martin RE (2015) Multi-method ensemble selection of spectral bands related to leaf biochemistry. Remote Sens Environ 164:57–65. https://doi.org/10.1016/j.rse.2015.03.033
Feng L, Zhang Z, Ma Y, Du Q, Williams P, Drewry J, Luck B (2020) Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sens 12:2028. https://doi.org/10.3390/rs12122028
Féret JB, Gitelson AA, Noble SD, Jacquemoud S (2017) PROSPECT-D: Towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ 193:204–215. https://doi.org/10.1016/j.rse.2017.03.004
Flénet F, Bouniols A, Saraiva C (1996) Sunflower response to a range of soil water contents. Eur J Agron 5(3–4):161–167. https://doi.org/10.1109/10.1016/S1161-0301(96)02006-0
Galar M, Fernandez A, Barrenechea E, Bustince H, Herrera F (2012) A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches. IEEE Trans Syst Man Cybernet Part C 42(4):463–484. https://doi.org/10.1109/tsmcc.2011.2161285
Gano B, Dembele JSB, Ndour A, Luquet D, Beurier G, Diouf D, Audebert A (2021) Using UAV borne, multi-spectral imaging for the field phenotyping of shoot biomass, leaf area index and height of west african sorghum varieties under two contrasted water conditions. Agronomy. 11(5):850. https://doi.org/10.3390/agronomy11050850
Gao Y, Lian X, Ge L (2022) Inversion model of surface bare soil temperature and water content based on UAV thermal infrared remote sensing. Infrared Phys Technol 125:104289. https://doi.org/10.1016/j.infrared.2022.104289
Ge H, Xiang H, Ma F, Li Z, Qiu Z, Tan Z, Du C (2021) Estimating plant nitrogen concentration of rice through fusing vegetation indices and color moments derived from UAV-RGB images. Remote Sens 13:1620. https://doi.org/10.3390/rs13091620
Ge X, Ding J, Jin X, Wang J, Chen X, Li X, Liu J, Xie B (2021) Estimating agricultural soil moisture content through UAV-based hyperspectral images in the arid region. Remote Sens 13(8):1562. https://doi.org/10.3390/rs13081562
Gerhards M, Schlerf M, Mallick K, Udelhoven T (2019) Challenges and future perspectives of multi-/hyperspectral thermal infrared remote sensing for crop water-stress detection: A review. Remote Sens 11(10):1240. https://doi.org/10.3390/rs11101240
Greaves HE, Vierling LA, Eitel JUH, Boelman NT, Magney TS, Prager CM, Griffin KL (2015) Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR. Remote Sens Environ 164:26–35. https://doi.org/10.1016/j.rse.2015.02.023
Guo J, Bai Q, Guo W, Bu Z, Zhang W (2022) Soil moisture content estimation in winter wheat planting area for multi-source sensing data using CNNR. Comput Electron Agric 193:106670. https://doi.org/10.1016/j.compag.2021.106670
Han L, Yang G, Dai H, Xu B, Yang H, Feng H, Li Z, Yang X (2019) Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods. 15:1. https://doi.org/10.1186/s13007-019-0394-z
Han D, Wang P, Tansey K, Zhou X, Zhang S, Tian H, Zhang J, Li H (2020) Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields. Comput Electron Agric 179:105833. https://doi.org/10.1016/j.compag.2020.105833
Han X, Wei Z, Chen H, Zhang B, Li Y, Du T (2021) Inversion of winter wheat growth parameters and yield under different water treatments based on UAV multispectral remote sensing. Front Plant Sci 12:609876. https://doi.org/10.3389/fpls.2021.609876
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet Part C SMC-3(6):610–621. https://doi.org/10.1109/tsmc.1973.4309314
He L, Cheng Y, Li Y, Li F, Fan K, Li Y (2021) An improved method for soil moisture monitoring with ensemble learning methods over the Tibetan Plateau. IEEE J Select Topics Appl Earth Observ Remote Sensing. 14:2833–2844. https://doi.org/10.1109/jstars.2021.3058325
Hegazi EH, Samak AA, Yang L, Huang R, Huang J (2023) Prediction of soil moisture content from sentinel-2 images Using Convolutional Neural Network (CNN). Agronomy. 13(3):656. https://doi.org/10.3390/agronomy13030656
Herr AW, Adak A, Carroll ME, Elango D, Kar S, Li C, Jones SE, Carter AH, Murray SC, Paterson A, Sankaran S, Singh A, Singh AK (2023) Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science. 63(4):1722–1749. https://doi.org/10.1002/csc2.21028
Ihuoma SO, Madramootoo CA (2017) Recent advances in crop water stress detection. Comput Electron Agric 141:267–275. https://doi.org/10.1016/j.compag.2017.07.026
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. Prec Agric 24(4):1439–1460. https://doi.org/10.1007/s11119-023-09997-5
Kim Y-C, Glick BR, Bashan Y, Ryu C-M (2012) Enhancement of plant drought tolerance by microbes, Plant Responses to Drought Stress. Springer Berlin Heidelberg. 383–413. https://doi.org/10.1007/978-3-642-32653-0_15
Lei F, Crow W, Shen H, Su C, Holmes T, Parinussa R, Wang G (2018) Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error. Remote Sens Environ 205:85–99. https://doi.org/10.1016/j.rse.2017.11.002
Li W, Niu Z, Chen H, Li D, Wu M, Zhao W (2016) Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol Indic 67:637–648. https://doi.org/10.1016/j.ecolind.2016.03.036
Li F, Li Y, Wang Y, Du Q (2020) Estimating hourly and continuous ground-level PM2.5 concentrations using an ensemble learning algorithm: the ST-stacking model. Atmos Environ 223:117242. https://doi.org/10.1016/j.atmosenv.2019.117242
Li G, Niu W, Sun J, Zhang W, Zhang E, Wang J (2021) Soil moisture and nitrogen content influence wheat yield through their effects on the root system and soil bacterial diversity under drip irrigation. Land Degrad Dev 32(10):3062–3076. https://doi.org/10.1002/ldr.3967
Liu Y, Liu S, Li J, Guo X, Wang S, Lu J (2019) Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images. Comput Electron Agric 166:105026. https://doi.org/10.1016/j.compag.2019.105026
Liu Q, Zhang F, Chen J, Li Y (2020) Water stress altered photosynthesis-vegetation index relationships for winter wheat. Agron J 112(4):2944–2955. https://doi.org/10.1002/agj2.20256
Liu F, Hu P, Zheng B, Duan T, Zhu B, Guo Y (2021) A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agric Forest Meteorol 296:108231. https://doi.org/10.1016/j.agrformet.2020.108231
Machichi MA, Mansouri LE, Imani Y, Bourja O, Lahlou O, Zennayi Y, Bourzeix F, Houmma HI, Hadria R (2023) Crop mapping using supervised machine learning and deep learning: a systematic literature review. Int J Remote Sens 44(8):2717–2753. https://doi.org/10.1080/01431161.2023.2205984
Maggio A, De Pascale S, Ruggiero C, Barbieri G (2005) Physiological response of field-grown cabbage to salinity and drought stress. Eur J Agron 23(1):57–67. https://doi.org/10.1016/j.eja.2004.09.004
Mahajan S, Tuteja N (2005) Cold, salinity and drought stresses: an overview. Arch Biochem Biophys 444(2):139–158. https://doi.org/10.1016/j.abb.2005.10.018
Mehta P, Wang CH, Day AGR, Richardson C, Bukov M, Fisher CK, Schwab DJ (2019) A high-bias, low-variance introduction to machine learning for physicists. Phys Rep 810:1–124. https://doi.org/10.1016/j.physrep.2019.03.001
Naimi AI, Balzer LB (2018) Stacked generalization: an introduction to super learning. Eur J Epidemiol 33(5):459–464. https://doi.org/10.1007/s10654-018-0390-z
Ndlovu HS, Odindi J, Sibanda M, Mutanga O, Clulow A, Chimonyo VGP, Mabhaudhi T (2021) A comparative estimation of maize leaf water content using machine learning techniques and unmanned aerial vehicle (UAV)-based proximal and remotely sensed data. Remote Sens 13(20):4091. https://doi.org/10.3390/rs13204091
Négyesi G, Szabo S, Buro B, Mohammed S, Loki J, Rajkai K, Holb IJ (2021) Influence of soil moisture and crust formation on soil evaporation rate: A wind tunnel experiment in Hungary. Agronomy. 11:935. https://doi.org/10.3390/agronomy11050935
Olson D, Anderson J (2021) Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agron J 113(2):971–992. https://doi.org/10.1002/agj2.20595
Ragu N, Teo J (2023) Object detection and classification using few-shot learning in smart agriculture: A scoping mini review. Front Sustain Food Syst 6:1039299. https://doi.org/10.3389/fsufs.2022.1039299
Raj R, Walker J, Pingale R, Nandan R, Naik B, Jagarlapudi A (2020) Leaf area index estimation using top-of-canopy airborne RGB images. Int J Appl Earth Observ Geoinform 96:102282. https://doi.org/10.1016/j.jag.2020.102282
Ren S, Guo B, Wu X, Zhang L, Ji M, Wang J (2021) Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain China. Comput Electron Agric 182:106049. https://doi.org/10.1016/j.compag.2021.106049
Ren S, Guo B, Wang Z, Wang J, Fang Q, Wang J (2022) Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress. Agric Water Manag 261:107333. https://doi.org/10.1016/j.agwat.2021.107333
Sagi O, Rokach L (2018) Ensemble learning: a survey. WIREs Data Mining and Knowledge Discov 8(4):e1249. https://doi.org/10.1002/widm.1249
Sha W, Hu K, Weng S (2023) Statistic and network features of RGB and hyperspectral imaging for determination of black root mold infection in apples. Foods. 12(8):1608. https://doi.org/10.3390/foods12081608
Shafizadeh-Moghadam H, Valavi R, Shahabi H, Chapi K, Shirzadi A (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manag 217:1–11. https://doi.org/10.1016/j.jenvman.2018.03.089
Shaikh TA, Rasool T, Lone RF (2022) Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Comput Electron Agric 198:107119. https://doi.org/10.1016/j.compag.2022.107119
Shao Y, Zhou H, Jiang L, Bao Y, He Y (2017) Using reflectance and gray-level texture for water content prediction in grape vines. Trans Asae Am Soc Agric Eng 60(1):207–213. https://doi.org/10.13031/trans.11794
Shao G, Han W, Zhang H, Zhang L, Wang Y, Zhang Y (2023) Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods. Agric Water Manag 276:108064. https://doi.org/10.1016/j.agwat.2022.108064
Sharma V, Tripathi AK, Mittal H (2022) Technological revolutions in smart farming: Current trends, challenges & future directions. Comp Electron Agric 201:107217. https://doi.org/10.1016/j.compag.2022.107217
Shu M, Dong Q, Fei S, Yang X, Zhu J, Meng L, Li B, Yuntao M (2022) Improved estimation of canopy water status in maize using UAV-based digital and hyperspectral images. Comput Electron Agric 197:106982. https://doi.org/10.1016/j.compag.2022.106982
Sibanda M, Mutanga O, Rouget M, Kumar L (2017) Estimating biomass of native grass grown under complex management treatments using WorldView-3 spectral derivatives. Remote Sens 9(1):55. https://doi.org/10.3390/rs9010055
Singh AK, Sreenivasu SVN, Mahalaxmi USBK, Sharma H, Patil DD, Asenso E, Khan R (2022) Hybrid feature-based disease detection in plant leaf using convolutional neural network, bayesian optimized SVM, and random forest classifier. J Food Qual 2022:1–16. https://doi.org/10.1155/2022/2845320
Singh A, Gaurav K, Sonkar GK, Lee C-C (2023) Strategies to measure soil moisture using traditional methods, automated sensors, remote sensing, and machine learning techniques: Review, bibliometric analysis, applications, research findings, and future directions. IEEE Access. 11:13605–13635. https://doi.org/10.1109/access.2023.3243635
Stricker MA, Dimai A (1996) Color indexing with weak spatial constraints. Storage and Retrieval for Still Image and Video Databases IV. 2670:29–40. https://doi.org/10.1117/12.234802
Stricker MA, Orengo M (1995) Similarity of color images. Storage and Retrieval for Image and Video Databases III International Society for Optics and Photonics. 2420:381–392. https://doi.org/10.1117/12.205308
Sun J, Shi S, Yang J, Gong W, Qiu F, Wang L, Du L, Chen B (2019) Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model. Agric Forest Meteorol 266–267:43–52. https://doi.org/10.1016/j.agrformet.2018.11.035
Ting KM, Witten IH (1999) Issues in stacked generalization. J Artic Intell Res 10:271–289. https://doi.org/10.1613/jair.594
Togneri R, Felipe dos Santos D, Camponogara G, Nagano H, Custódio G, Prati R, Fernandes S, Kamienski C (2022) Soil moisture forecast for smart irrigation: The primetime for machine learning. Expert Systems with Applications. 207. https://doi.org/10.1016/j.eswa.2022.117653.
Tong H, Nikoloski Z (2021) Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data. J Plant Physiol 257:153354. https://doi.org/10.1016/j.jplph.2020.153354
Trout TJ, DeJonge KC (2021) Evapotranspiration and water stress coefficient for deficit-Irrigated maize. Journal of Irrigation and Drainage Engineering. 147(10). https://doi.org/10.1061/(asce)ir.1943-4774.0001600
Tyralis H, Papacharalampous G, Burnetas A, Langousis A (2019) Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS. J Hydrol 577:123957. https://doi.org/10.1016/j.jhydrol.2019.123957
Wang X, Zhao C, Guo N, Li Y, Jian S, Yu K (2015) Determining the canopy water stress for spring wheat using canopy hyperspectral reflectance data in Loess Plateau semiarid regions. Spectrosc Lett 48(7):492–498. https://doi.org/10.1080/00387010.2014.909495
Weiss M, Jacob F, Duveiller G (2020) Remote sensing for agricultural applications: A meta-review. Remote Sens Environ 236:111402. https://doi.org/10.1016/j.rse.2019.111402
Westhues CC, Mahone GS, da Silva S, Thorwarth P, Schmidt M, Richter JC, Simianer H, Beissinger TM (2021) Prediction of maize phenotypic traits with genomic and environmental predictors using gradient boosting frameworks. Front Plant Sci 12:699589. https://doi.org/10.3389/fpls.2021.699589
Xu K, Yang W, Ye H (2023) Thermal infrared reflectance characteristics of natural leaves in 8–14 -μm region: Mechanistic modeling and relationships with leaf water content. Remote Sens Environ 294:113631. https://doi.org/10.1016/j.rse.2023.113631
Yan G, Hu R, Luo J, Weiss M, Jiang H, Mu X, Xie D, Zhang W (2019) Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives. Agric Forest Meteorol 265:390–411. https://doi.org/10.1016/j.agrformet.2018.11.033
Yang G, Li C, Wang Y, Yuan H, Feng H, Xu B, Yang X (2017) The DOM generation and precise radiometric calibration of a UAV-mounted miniature snapshot hyperspectral imager. Remote Sens 9(7):642. https://doi.org/10.3390/rs9070642
Yang Q, Shi L, Han J, Zha Y, Zhu P (2019) Deep convolutional neural networks for rice grain yield estimation at the ripening stage using UAV-based remotely sensed images. Field Crops Res 235:142–153. https://doi.org/10.1016/j.fcr.2019.02.022
Yang N, Zhang Z, Zhang J, Guo Y, Yang X, Yu G, Bai X, Chen J, Chen Y, Shi L, Li X (2023) Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index. Comput Electron Agric 214:108294. https://doi.org/10.1016/j.compag.2023.108294
Yu K, Lenz-Wiedemann V, Chen X, Bareth G (2014) Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects. ISPRS J Photogramm Remote Sens 97:58–77. https://doi.org/10.1016/j.isprsjprs.2014.08.005
Yue J, Feng H, Jin X, Yuan H, Li Z, Zhou C, Yang G, Tian Q (2018) A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and sigh-definition digital camera. Remote Sens 10(7):1138. https://doi.org/10.3390/rs10071138
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 Sens 10(2):66. https://doi.org/10.3390/rs10010066
Yue J, Yang G, Tian Q, Feng H, Xu K, Zhou C (2019) Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS J Photogramm Remote Sens 150:226–244. https://doi.org/10.1016/j.isprsjprs.2019.02.022
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. https://doi.org/10.1016/j.compag.2023.107627
Zhang Y, Liu J, Shen W (2022) A review of ensemble learning algorithms used in remote sensing applications. Appl Sci 12(17):8654. https://doi.org/10.3390/app12178654
Zhang Y, Han W, Zhang H, Niu X, Shao G (2023) Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms. J Hydrol 617:129086. https://doi.org/10.1016/j.jhydrol.2023.129086
Zheng H, Cheng T, Yao X, Deng X, Tian Y, Cao W, Zhu Y (2016) Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Res 198:131–139. https://doi.org/10.1016/j.fcr.2016.08.027
Zhiipao RR, Pooniya V, Kumar D, Biswakarma N, Shivay YS, Dass A, Kumar Bainsla N, Lakhena KK (2023) Above and below-ground growth, accumulated dry matter and nitrogen remobilization of wheat (Triticum aestivum) genotypes grown in PVC tubes under well- and deficit-watered conditions. Front Plant Sci 14:1087343. https://doi.org/10.3389/fpls.2023.1087343
Zhou Y, Lao C, Yang Y, Zhang Z, Chen H, Chen Y, Chen J, Ning J, Yang N (2021) Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices. Agric Water Manag 256:107076. https://doi.org/10.1016/j.agwat.2021.107076
Zhou Z, Majeed Y, Diverres Naranjo G, Gambacorta EMT (2021) Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Comput Electron Agric 182:106019. https://doi.org/10.1016/j.compag.2021.106019
Zhu S, Cui N, Zhou J, Xue J, Wang Z, Wu Z, Wang M, Deng Q (2023) Digital mapping of root-zone soil moisture using UAV-based multispectral data in a kiwifruit orchard of northwest China. Remote Sens 15(3):646. https://doi.org/10.3390/rs15030646
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This research was supported by the National Key Research and Development Program of China (2022YFD1900404), the National Natural Science Foundation of China (52179044, 52279047).
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Yang, N., Zhang, Z., Ding, B. et al. Evaluation of winter-wheat water stress with UAV-based multispectral data and ensemble learning method. Plant Soil 497, 647–668 (2024). https://doi.org/10.1007/s11104-023-06422-8
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DOI: https://doi.org/10.1007/s11104-023-06422-8