Precision crop mapping: within plant canopy discrimination of crop and soil using multi-sensor hyperspectral imagery.
Drones
Endmembers
Hyperspectral imagery
Precision agriculture
Spectral unmixing
Sub-canopy crop-soil discrimination
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 Oct 2024
22 Oct 2024
Historique:
received:
12
03
2024
accepted:
04
10
2024
medline:
23
10
2024
pubmed:
23
10
2024
entrez:
22
10
2024
Statut:
epublish
Résumé
Leveraging diverse optomechanical and imaging technologies for precision agriculture applications is gaining attention in emerging economies. The precise spatial detection of plant objects in farms is crucial for optimizing plant-level nutrition and managing pests and diseases. High-resolution remote sensors mounted on drones have been increasingly deployed for large-scale crop mapping and field variability characterization. While field-level crop identification and crop-soil discrimination have been studied extensively, within-plant canopy discrimination of crop and soil has not been explored in real agricultural farms. The objectives of this study are: (i) adoption and assessment of spectral unmixing for discriminating crop and soil at within-plant canopy level, and (ii) generation of benchmark terrestrial and drone-based hyperspectral datasets for plant or sub-plant level discrimination using various spectral mixture modelling approaches and sources of endmembers. We acquired hyperspectral imagery of vegetable crops using a frame-based sensor mounted on a drone flying at different heights. Further, several linear, non-linear, and sparse-based spectral unmixing methods were used to discriminate plant and soil based on spectral signatures (endmembers) extracted from different spectral libraries prepared using in situ or field, ground-based, and drone-based hyperspectral imagery. The results, validated against pixel-to-pixel ground truth data, indicate an overall crop-soil discrimination accuracy of 99-100%, subject to a combination of endmember source and flying height. The influences of different endmember sources, spatial resolution as indicated by flying height, and inversion algorithms on the quality of estimated abundances are assessed from a verifiable and functionally relevant perspective. The generated hyperspectral datasets and ground truth data can be used for developing and testing new methods for sub-canopy level soil-crop discrimination in various agricultural applications of remote sensing.
Identifiants
pubmed: 39438520
doi: 10.1038/s41598-024-75394-1
pii: 10.1038/s41598-024-75394-1
doi:
Substances chimiques
Soil
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24903Informations de copyright
© 2024. The Author(s).
Références
Cuaran, J. & Leon, J. Crop monitoring using unmanned aerial vehicles: A review. Agric. Rev. https://doi.org/10.18805/ag.R-180 (2021).
doi: 10.18805/ag.R-180
Suchi, S. D., Menon, A., Malik, A., Hu, J. & Gao, J. Crop Identification based on remote sensing data using machine learning approaches for Fresno County, California. In IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService) 115–124 (IEEE, Oxford, United Kingdom, 2021). https://doi.org/10.1109/BigDataService52369.2021.00019
Wu, F., Wu, B., Zhang, M., Zeng, H. & Tian, F. Identification of crop type in crowdsourced road view photos with deep convolutional neural network. Sensors 21, 1165 (2021).
doi: 10.3390/s21041165
pubmed: 33562266
pmcid: 7914883
Aznar-Sánchez, J. A., Velasco-Muñoz, J. F., López-Felices, B. & Román-Sánchez, I. M. An analysis of global research trends on greenhouse technology: Towards a sustainable agriculture. Int. J. Environ. Res. Public Health 17, 664 (2020).
doi: 10.3390/ijerph17020664
pubmed: 31968567
pmcid: 7013810
Kavga, A., Thomopoulos, V., Barouchas, P., Stefanakis, N. & Liopa-Tsakalidi, A. Research on innovative training on smart greenhouse technologies for economic and environmental sustainability. Sustainability 13, 10536 (2021).
doi: 10.3390/su131910536
Yang, N. et al. Large-scale crop mapping based on machine learning and parallel computation with grids. Remote Sens. 11, 1500 (2019).
doi: 10.3390/rs11121500
Jiang, Y. et al. Large-scale and high-resolution crop mapping in China using sentinel-2 satellite imagery. Agriculture 10, 433 (2020).
doi: 10.3390/agriculture10100433
Yan, S. et al. Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids. Int. J. Appl. Earth Obs. Geoinform. 103, 102485 (2021).
Liu, X. et al. Large-scale crop mapping from multisource remote sensing images in Google earth engine. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 414–427 (2020).
doi: 10.1109/JSTARS.2019.2963539
Moumni, A. & Lahrouni, A. Machine learning-based classification for crop-type mapping using the fusion of high-resolution satellite imagery in a semiarid area. Scientifica 1–20 (2021).
Turkoglu, M. O. et al. Crop mapping from image time series: Deep learning with multi-scale label hierarchies. Remote Sens. Environ. 264, 112603 (2021).
doi: 10.1016/j.rse.2021.112603
Alami Machichi, M. et al. Crop mapping using supervised machine learning and deep learning: A systematic literature review. Int. J. Remote Sens. 44, 2717–2753 (2023).
doi: 10.1080/01431161.2023.2205984
Khan, H. R. et al. Early identification of crop type for smallholder farming systems using deep learning on time-series sentinel-2 imagery. Sensors 23, 1779 (2023).
doi: 10.3390/s23041779
pubmed: 36850377
pmcid: 9967001
Liu, Y. et al. Remote-sensing estimation of potato above-ground biomass based on spectral and spatial features extracted from high-definition digital camera images. Comput. Electron. Agric. 198, 107089 (2022).
doi: 10.1016/j.compag.2022.107089
Liu, Y. et al. Estimating potato above-ground biomass by using integrated unmanned aerial system-based optical, structural, and textural canopy measurements. Comput. Electron. Agric. 213, 108229 (2023).
doi: 10.1016/j.compag.2023.108229
Liu, Y. et al. Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques. Comput. Electron. Agric. 218, 108699 (2024).
doi: 10.1016/j.compag.2024.108699
Apan, A. A. et al. Spectral discrimination and separability analysis of agricultural crops and soil attributes using ASTER imagery. 17 (2002).
Viscarra Rossel, R. A. & Webster, R. Discrimination of Australian soil horizons and classes from their visible-near infrared spectra. Eur. J. Soil Sci. 62, 637–647 (2011).
doi: 10.1111/j.1365-2389.2011.01356.x
Andújar, D. et al. Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor. Sensors 13, 14662–14675 (2013).
doi: 10.3390/s131114662
pubmed: 24172283
pmcid: 3871132
Falco, N. et al. Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery. Sci. Rep. 11, 7046 (2021).
doi: 10.1038/s41598-021-86480-z
pubmed: 33782488
pmcid: 8007594
Misbah, K., Laamrani, A., Khechba, K., Dhiba, D. & Chehbouni, A. Multi-sensors remote sensing applications for assessing, monitoring, and mapping NPK content in soil and crops in African agricultural land. Remote Sens. 14, 81 (2021).
doi: 10.3390/rs14010081
Yang, X. et al. Soil nutrient estimation and mapping in Farmland based on UAV imaging spectrometry. Sensors 21, 3919 (2021).
doi: 10.3390/s21113919
pubmed: 34204160
pmcid: 8201019
Hashemi-Beni, L., Gebrehiwot, A., Karimoddini, A., Shahbazi, A. & Dorbu, F. Deep convolutional neural networks for weeds and crops discrimination from UAS imagery. Front. Remote Sens. 3, 755939 (2022).
doi: 10.3389/frsen.2022.755939
Xue, J. & Su, B. Significant remote sensing vegetation indices: A review of developments and applications. J. Sens. 1–17 (2017). (2017).
Le, N. T., Apopei, V., Alameh, K. & B. & Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods. Inf. Process. Agric. 6, 116–131 (2019).
Guo, A. et al. Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sens. 12, 1419 (2020).
doi: 10.3390/rs12091419
Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36, 1627–1639 (1964).
doi: 10.1021/ac60214a047
Van der Meer, F. D. & De Jong, S. M. Imaging Spectrometry: Basic Principles and Prospective Applications Vol. 4 (Springer Science & Business Media, 2011).
Keshava, N., Kerekes, J., Manolakis, D. & Shaw, G. An algorithm taxonomy for hyperspectral unmixing. 22 (2000).
Keshava, N. A survey of spectral unmixing algorithms. Linc. Lab. J. 14, 55–78 (2003).
Heylen, R. & Scheunders, P. A multilinear mixing model for nonlinear spectral unmixing. IEEE Trans. Geosci. Remote Sens. 54, 240–251 (2016).
doi: 10.1109/TGRS.2015.2453915
Iordache, M. D., Bioucas-Dias, J. M. & Plaza, A. Sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 49, 2014–2039 (2011).
doi: 10.1109/TGRS.2010.2098413
Iordache, M. D., Bioucas-Dias, J. M. & Plaza, A. Collaborative sparse regression for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 52, 341–354 (2014).
doi: 10.1109/TGRS.2013.2240001
Bioucas-Dias, J. M. & Figueiredo, M. A. T. Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing. (2012). https://arxiv.org/abs/1002.4527 Math.
Hapke, B. Theory of Reflectance and Emittance Spectroscopy (Cambridge University Press, 2012). https://doi.org/10.1017/CBO9781139025683
Li, Z. et al. Subpixel change detection based on radial basis function with abundance image difference measure for remote sensing images. Remote Sens. 13, 868 (2021).
doi: 10.3390/rs13050868
Nguyen, C. T., Chidthaisong, A., Kieu Diem, P. & Huo, L. Z. A Modified bare soil index to identify bare land features during agricultural fallow-period in Southeast Asia using Landsat 8. Land 10, 231 (2021).
Bhatt, J. S. & Joshi, M. V. Deep learning in hyperspectral unmixing: A review. In IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium 2189–2192 (2020). https://doi.org/10.1109/IGARSS39084.2020.9324546
Cavalli, R. M. Spatial validation of spectral unmixing results: A systematic review. Remote Sens. 15, 2822 (2023).
doi: 10.3390/rs15112822
Zaman, Z., Ahmed, S. B. & Malik, M. I. Analysis of hyperspectral data to develop an approach for document images. Sensors 23, 6845 (2023).
doi: 10.3390/s23156845
pubmed: 37571629
pmcid: 10422312
Shao, Y., Lan, J., Zhang, Y. & Zou, J. Spectral unmixing of hyperspectral remote sensing imagery via preserving the intrinsic structure invariant. Sensors 18, 3528 (2018).
doi: 10.3390/s18103528
pubmed: 30340435
pmcid: 6211137