Review

Hyperspectral imaging for rice cultivation: Applications, methods and challenges

  • Received: 22 September 2020 Accepted: 30 November 2020 Published: 20 January 2021
  • Hyperspectral imaging has become a valuable remote sensing tool due to the development of advanced remote acquisition systems with high spatial and spectral resolution, and the continuous developments on more efficient computing resources to handle the high volume of data. For this reason, hyperspectral image analysis has found important uses in precision agriculture, where the health status of crops in various stages of the production process can be assessed from their spectral signatures. This has similarly been the case for rice cultivation, which represents one of the most valuable crops worldwide in terms of gross production value, global consumption rates, and food security reserves. To maximize the productivity of this activity and minimize economic and food crop losses, various precision agriculture techniques to optimize yields by managing production inputs and monitoring plant health have been developed. Such applications include landcover classification, cultivar identification, nitrogen level assessment, chlorophyll content estimation and the identification of various factors, such as the presence of pests, weeds, disease or pollutants. The current work highlights and summarizes various aspects of interest of the main studies on hyperspectral imaging applications for rice cultivation. For instance, several tables summarize the most relevant work on the application of hyperspectral imaging for rice cultivation based on their acquisition methods, spectral region, rice species, and inferred magnitudes, among other parameters. In addition, we identify challenges across the field that limit the widespread deployment of hyperspectral imaging applications. Among these challenges, adequate modeling of various dynamic local factors and their influence on the analysis is a main concern. The main objective of this review is to provide a reference for future works that addresses the main challenges, and accelerate the development of deployable end user technologies to meet current global Sustainable Development Goals, in a manner that is resilient towards the increasingly dynamic growing conditions of rice plants expected by global climate change.

    Citation: Fernando Arias, Maytee Zambrano, Kathia Broce, Carlos Medina, Hazel Pacheco, Yerenis Nunez. Hyperspectral imaging for rice cultivation: Applications, methods and challenges[J]. AIMS Agriculture and Food, 2021, 6(1): 273-307. doi: 10.3934/agrfood.2021018

    Related Papers:

  • Hyperspectral imaging has become a valuable remote sensing tool due to the development of advanced remote acquisition systems with high spatial and spectral resolution, and the continuous developments on more efficient computing resources to handle the high volume of data. For this reason, hyperspectral image analysis has found important uses in precision agriculture, where the health status of crops in various stages of the production process can be assessed from their spectral signatures. This has similarly been the case for rice cultivation, which represents one of the most valuable crops worldwide in terms of gross production value, global consumption rates, and food security reserves. To maximize the productivity of this activity and minimize economic and food crop losses, various precision agriculture techniques to optimize yields by managing production inputs and monitoring plant health have been developed. Such applications include landcover classification, cultivar identification, nitrogen level assessment, chlorophyll content estimation and the identification of various factors, such as the presence of pests, weeds, disease or pollutants. The current work highlights and summarizes various aspects of interest of the main studies on hyperspectral imaging applications for rice cultivation. For instance, several tables summarize the most relevant work on the application of hyperspectral imaging for rice cultivation based on their acquisition methods, spectral region, rice species, and inferred magnitudes, among other parameters. In addition, we identify challenges across the field that limit the widespread deployment of hyperspectral imaging applications. Among these challenges, adequate modeling of various dynamic local factors and their influence on the analysis is a main concern. The main objective of this review is to provide a reference for future works that addresses the main challenges, and accelerate the development of deployable end user technologies to meet current global Sustainable Development Goals, in a manner that is resilient towards the increasingly dynamic growing conditions of rice plants expected by global climate change.


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    [1] Chauhan BS, Jabran K, Mahajan G (2017) Rice production worldwide, Springer.
    [2] Hu EA, Pan A, Malik V, et al. (2012) White rice consumption and risk of type 2 diabetes: Meta-analysis and systematic review. Bmj 344: e1454. doi: 10.1136/bmj.e1454
    [3] Seck PA, Diagne A, Mohanty S, et al. (2012) Crops that feed the world 7: Rice. Food Secur 4: 7-24. doi: 10.1007/s12571-012-0168-1
    [4] Fuller DQ, Sato YI, Castillo C, et al. (2010) Consilience of genetics and archaeobotany in the entangled history of rice. Archaeol Anthropol Sci 2: 115-131. doi: 10.1007/s12520-010-0035-y
    [5] Food and Agriculture Organization of the United Nations (2018) Rice market monitor 2018, Rice Market Monitor XXI.
    [6] Tan BL, Norhaizan ME (2020) Rice demands: A brief description, Rice by-products: Phytochemicals and food products application, Springer, 7-11.
    [7] Peng S, Tang Q, Zou Y (2009) Current status and challenges of rice production in China. Plant Prod Sci 12: 3-8. doi: 10.1626/pps.12.3
    [8] Van Nguyen N, Ferrero A (2006) Meeting the challenges of global rice production. Paddy Water Environ 4: 1-9. doi: 10.1007/s10333-005-0031-5
    [9] Barker R, Dawe D, Tuong T, et al. (2020) The outlook for water resources in the year 2020: Challenges for research on water management in rice production. Southeast Asia 1: 1-5.
    [10] Mo C, Lim J, Kwon SW, et al. (2017) Hyperspectral imaging and partial least square discriminant analysis for geographical origin discrimination of white rice. J Biosyst Eng 42: 293-300.
    [11] Tong Q, Swallow B, Zhang L, et al. (2019) The roles of risk aversion and climate-smart agriculture in climate risk management: Evidence from rice production in the Jianghan Plain, China. Clim Risk Manage 26: 100199. doi: 10.1016/j.crm.2019.100199
    [12] Balasubramanian V, Sie M, Hijmans R, et al. (2007) Increasing rice production in sub-Saharan Africa: Challenges and opportunities. Adv Agron 94: 55-133. doi: 10.1016/S0065-2113(06)94002-4
    [13] Chen J, Zou W, Meng L, et al. (2019) Advances in the uptake and transport mechanisms and QTLs mapping of cadmium in rice. Int J Mol Sci 20: 3417. doi: 10.3390/ijms20143417
    [14] Jin XB, Yu XH, Wang XY, et al. (2020) Deep learning predictor for sustainable precision agriculture based on internet of things system. Sustainability 12: 1433. doi: 10.3390/su12041433
    [15] Salam A, Shah S (2019) Internet of things in smart agriculture: Enabling technologies, 2019 IEEE 5th world forum on internet of things (WF-IoT), 692-695.
    [16] Kundu M, Krishnan P, Kotnala R, et al. (2019) Recent developments in biosensors to combat agricultural challenges and their future prospects. Trends Food Sci Technol 88: 157-178. doi: 10.1016/j.tifs.2019.03.024
    [17] Thakur D, Kumar Y, Kumar A, et al. (2019) Applicability of wireless sensor networks in precision agriculture: A review. Wireless Pers Commun 107: 471-512. doi: 10.1007/s11277-019-06285-2
    [18] International Telecommunications Union and XPRIZE Foundation (2017) AI for good global summit report, Geneva, Switzerland, United Nations.
    [19] Bejaoui C, Guitton A, Kachouri A (2017) Efficient data monitoring of rice-fields based on WMSN, 2017 IEEE/ACS 14th international conference on computer systems and applications (AICCSA), 1090-1094.
    [20] Chen WL, Lin YB, Ng FL, et al. (2019) RiceTalk: Rice blast detection using internet of things and artificial intelligence technologies. IEEE Internet Things J 7: 1001-1010. doi: 10.1109/JIOT.2019.2947624
    [21] Gao Z, Li W, Zhu Y, et al. (2018) Wireless channel propagation characteristics and modeling research in rice field sensor networks. Sensors 18: 3116. doi: 10.3390/s18093116
    [22] Sabet FS, Hosseini M, Khabbaz H, et al. (2017) FRET-based aptamer biosensor for selective and sensitive detection of aflatoxin B1 in peanut and rice. Food Chem 220: 527-532. doi: 10.1016/j.foodchem.2016.10.004
    [23] Hung T, Byun W (2006) Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regress. Eur J Agron 24: 349-356. doi: 10.1016/j.eja.2006.01.001
    [24] Intaravanne Y, Sumriddetchkajorn S (2015) Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer. Comput Electron Agric 116: 228-233. doi: 10.1016/j.compag.2015.07.005
    [25] Wang L, Liu D, Pu H, et al. (2015) Use of hyperspectral imaging to discriminate the variety and quality of rice. Food Anal Methods 8: 515-523. doi: 10.1007/s12161-014-9916-5
    [26] Caballero D, Calvini R, Amigo JM (2020) Hyperspectral imaging in crop fields: Precision agriculture, Data handling in science and technology, Elsevier, 453-473.
    [27] Lu B, Dao PD, Liu J, et al. (2020) Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens 12: 2659. doi: 10.3390/rs12162659
    [28] Foster DH, Amano K (2019) Hyperspectral imaging in color vision research: tutorial. JOSA A 36: 606-627. doi: 10.1364/JOSAA.36.000606
    [29] Khan MJ, Khan HS, Yousaf A, et al. (2018) Modern trends in hyperspectral image analysis: A review. IEEE Access 6: 14118-14129. doi: 10.1109/ACCESS.2018.2812999
    [30] Paoletti M, Haut J, Plaza J, et al. (2019) Deep learning classifiers for hyperspectral imaging: A review. ISPRS J Photogramm Remote Sens 158: 279-317. doi: 10.1016/j.isprsjprs.2019.09.006
    [31] Zhang K, Liu X, Ma Y, et al. (2020) A comparative assessment of measures of leaf nitrogen in rice using two leaf-clip meters. Sensors 20: 175. doi: 10.3390/s20010175
    [32] Zhang M, Lin H, Wang G, et al. (2018) Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sens 10: 1840. doi: 10.3390/rs10111840
    [33] Zheng H, Zhou X, He J, et al. (2020) Early season detection of rice plants using RGB, NIR-GB and multispectral images from unmanned aerial vehicle (UAV). Comput Electron Agric 169: 105223. doi: 10.1016/j.compag.2020.105223
    [34] Baek I, Kim MS, Cho BK, et al. (2019) Selection of optimal hyperspectral wavebands for detection of discolored, diseased rice seeds. Appl Sci 9: 1027. doi: 10.3390/app9051027
    [35] Fan Y, Wang T, Qiu Z, et al. (2017) Fast detection of striped stem-borer (Chilo suppressalis Walker) infested rice seedling based on visible/near-infrared hyperspectral imaging system. Sensors 17: 2470. doi: 10.3390/s17112470
    [36] Huang J, Liao H, Zhu Y, et al. (2012) Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis). Comput Electron Agric 82: 100-107. doi: 10.1016/j.compag.2012.01.002
    [37] Schneider P, Asch F (2020) Rice production and food security in Asian Mega deltas-A review on characteristics, vulnerabilities and agricultural adaptation options to cope with climate change. J Agron Crop Sci 206: 491-503. doi: 10.1111/jac.12415
    [38] Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327: 828-831. doi: 10.1126/science.1183899
    [39] Ehleringer JR, Cerling TE, Helliker BR (1997) C 4 photosynthesis, atmospheric CO2, and climate. Oecologia 112: 285-299. doi: 10.1007/s004420050311
    [40] Sonnino A (1994) Agricultural biomass production is an energy option for the future. Renewable Energy 5: 857-865. doi: 10.1016/0960-1481(94)90105-8
    [41] Lichtenthaler HK, Rinderle U (1988) The role of chlorophyll fluorescence in the detection of stress conditions in plants. CRC Crit Rev Anal Chem 19: S29-S85. doi: 10.1080/15476510.1988.10401466
    [42] Mafakheri A, Siosemardeh A, Bahramnejad B, et al. (2010) Effect of drought stress on yield, proline and chlorophyll contents in three chickpea cultivars. Aust J Crop Sci 4: 580.
    [43] Bannister T (1974) Production equations in terms of chlorophyll concentration, quantum yield, and upper limit to production. Limnol Oceanogr 19: 1-12. doi: 10.4319/lo.1974.19.1.0001
    [44] Wood C, Reeves D, Himelrick D (1993) Relationships between chlorophyll meter readings and leaf chlorophyll concentration, N status, and crop yield: A review, Proceedings of the agronomy society of New Zealand, 1-9.
    [45] Makino A (2011) Photosynthesis, grain yield, and nitrogen utilization in rice and wheat. Plant Physiol 155: 125-129. doi: 10.1104/pp.110.165076
    [46] Zhou K, Cheng T, Zhu Y, et al. (2018) Assessing the impact of spatial resolution on the estimation of leaf nitrogen concentration over the full season of paddy rice using near-surface imaging spectroscopy data. Frontiers in plant science 9: 964. doi: 10.3389/fpls.2018.00964
    [47] Matsushima S (1993) Researches on the requirements for achieving high yields in rice. Sci Rice Plant 2: 737-747.
    [48] Inoue Y, Sakaiya E, Zhu Y, et al. (2012) Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens Environ 126: 210-221. doi: 10.1016/j.rse.2012.08.026
    [49] Lee YJ, Yang CM, Chang KW, et al. (2008) A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy. Agron J 100: 205-212. doi: 10.2134/agronj2007.0018
    [50] Sun J, Lu X, Mao H, et al. (2017) A method for rapid identification of rice origin by hyperspectral imaging technology. J Food Process Eng 40: e12297. doi: 10.1111/jfpe.12297
    [51] Raines CA (2011) Increasing photosynthetic carbon assimilation in C3 plants to improve crop yield: Current and future strategies. Plant Physiol 155: 36-42. doi: 10.1104/pp.110.168559
    [52] Nowak B, Nesme T, David C, et al. (2015) Nutrient recycling in organic farming is related to diversity in farm types at the local level. Agric, Ecosyst Environ 204: 17-26. doi: 10.1016/j.agee.2015.02.010
    [53] Song YQ, Zhao X, Su HY, et al. (2018) Predicting spatial variations in soil nutrients with hyperspectral remote sensing at regional scale. Sensors 18: 3086. doi: 10.3390/s18093086
    [54] Luo JS, Huang J, Zeng DL, et al. (2018) A defensin-like protein drives cadmium efflux and allocation in rice. Nat Commun 9: 1-9. doi: 10.1038/s41467-017-02088-w
    [55] Muehe EM, Wang T, Kerl CF, et al. (2019) Rice production threatened by coupled stresses of climate and soil arsenic. Nat Commun 10: 1-10. doi: 10.1038/s41467-019-12946-4
    [56] Sharma RK, Agrawal M (2005) Biological effects of heavy metals: An overview. J Environ Biol 26: 301-313.
    [57] Xie P, Deng J, Zhang H, et al. (2015) Effects of cadmium on bioaccumulation and biochemical stress response in rice (Oryza sativa L.). Ecotoxicol Environ Saf 122: 392-398. doi: 10.1016/j.ecoenv.2015.09.007
    [58] Xue D, Jiang H, Deng X, et al. (2014) Comparative proteomic analysis provides new insights into cadmium accumulation in rice grain under cadmium stress. J Hazard Mater 280: 269-278. doi: 10.1016/j.jhazmat.2014.08.010
    [59] Hanak E, Lund JR (2012) Adapting California's water management to climate change. Clim Change 111: 17-44. doi: 10.1007/s10584-011-0241-3
    [60] Iglesias A, Garrote L (2015) Adaptation strategies for agricultural water management under climate change in Europe. Agric Water Manage 155: 113-124. doi: 10.1016/j.agwat.2015.03.014
    [61] Ahangarha M, Seydi ST, Shahhoseini R (2019) Hyperspectral change detection in wetland and water-body areas based on machine learning. Int Arch Photogramm, Remote Sens Spat Inf Sci XLⅡ-4/W18: 1-19.
    [62] León CD, Kosow H, Zahumensky Y, et al. (2019) Solutions and planning tools for water supply and wastewater management in prosperous regions tackling water scarcity, Mid-term Conference-Frankfurt am main, Germany 20-21 February 2019, 28.
    [63] Liew S, Choo C, Lau J, et al. (2019) Monitoring water quality in Singapore reservoirs with hyperspectral remote sensing technology. Water Pract Technol 14: 118-125. doi: 10.2166/wpt.2018.119
    [64] Prošek J, Gdulová K, Barták V, et al. (2020) Integration of hyperspectral and LiDAR data for mapping small water bodies. Int J Appl Earth Obs Geoinformation 92: 102181. doi: 10.1016/j.jag.2020.102181
    [65] Ge X, Wang J, Ding J, et al. (2019) Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. Peer J 7: e6926. doi: 10.7717/peerj.6926
    [66] Huo H, Li Z-L, Xing Z (2019) Temperature/emissivity separation using hyperspectral thermal infrared imagery and its potential for detecting the water content of plants. Int J Remote Sens 40: 1672-1692. doi: 10.1080/01431161.2018.1513668
    [67] Hurley SP, Horney M, Drake A (2019) Using hyperspectral imagery to detect water stress in vineyards, Autonomous air and ground sensing systems for agricultural optimization and phenotyping IV, 1100807.
    [68] Krishna G, Sahoo RN, Singh P, et al. (2019) Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing. Agric Water Manage 213: 231-244. doi: 10.1016/j.agwat.2018.08.029
    [69] Zhang Z, Lou Y, Li R, et al. (2019) Estimation of relative water content in rice panicle based on hyperspectral vegetation indexes under water saving irrigation. Spectrosc Lett 52: 150-158. doi: 10.1080/00387010.2019.1594309
    [70] Manolakis DG, Lockwood RB, Cooley TW (2016) Hyperspectral imaging remote sensing: Physics, sensors, and algorithms, Cambridge University Press.
    [71] Ahmed MR, Yasmin J, Mo C, et al. (2016) Outdoor applications of hyperspectral imaging technology for monitoring agricultural crops: A review. J Biosyst Eng 41: 396-407. doi: 10.5307/JBE.2016.41.4.396
    [72] Fei B (2020) Hyperspectral imaging in medical applications, Data handling in science and technology, Elsevier, 523-565.
    [73] Ma J, Sun DW, Pu H, et al. (2019) Advanced techniques for hyperspectral imaging in the food industry: principles and recent applications. Annu Rev Food Sci Technol 10: 197-220. doi: 10.1146/annurev-food-032818-121155
    [74] Zheng H, Cheng T, Li D, et al. (2018) Evaluation of RGB, color-infrared and multispectral images acquired from unmanned aerial systems for the estimation of nitrogen accumulation in rice. Remote Sens 10: 824. doi: 10.3390/rs10060824
    [75] Mahajan G, Pandey R, Sahoo R, et al. (2017) Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing. Precis Agric 18: 736-761. doi: 10.1007/s11119-016-9485-2
    [76] Zhu Y, Tian Y, Yao X, et al. (2007) Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Prod Sci 10: 400-411. doi: 10.1626/pps.10.400
    [77] Behmann J, Steinrücken J, Plümer L (2014) Detection of early plant stress responses in hyperspectral images. ISPRS J Photogramm Remote Sens 93: 98-111. doi: 10.1016/j.isprsjprs.2014.03.016
    [78] Römer C, Wahabzada M, Ballvora A, et al. (2012) Early drought stress detection in cereals: Simplex volume maximisation for hyperspectral image analysis. Funct Plant Biol 39: 878-890. doi: 10.1071/FP12060
    [79] Pearlman JS, Barry PS, Segal CC, et al. (2003) Hyperion, a space-based imaging spectrometer. IEEE Trans Geosci Remote Sens 41: 1160-1173. doi: 10.1109/TGRS.2003.815018
    [80] Green RO, Eastwood ML, Sarture CM, et al. (1998) Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens Environ 65: 227-248. doi: 10.1016/S0034-4257(98)00064-9
    [81] Banerjee BP, Raval S, Cullen P (2020) UAV-hyperspectral imaging of spectrally complex environments. Int J Remote Sens 41: 4136-4159. doi: 10.1080/01431161.2020.1714771
    [82] Wiegmann M, Backhaus A, Seiffert U, et al. (2019) Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging. PloS One 14: e0224491. doi: 10.1371/journal.pone.0224491
    [83] Smith ML, Ollinger SV, Martin ME, et al. (2002) Direct estimation of aboveground forest productivity through hyperspectral remote sensing of canopy nitrogen. Ecol Appl 12: 1286-1302. doi: 10.1890/1051-0761(2002)012[1286:DEOAFP]2.0.CO;2
    [84] Yao L, Wang Q, Yang J, et al. (2019) UAV-Borne dual-band sensor method for monitoring physiological crop status. Sensors 19: 816. doi: 10.3390/s19040816
    [85] Fan L, Zhao J, Xu X, et al. (2019) Hyperspectral-based estimation of leaf nitrogen content in corn using optimal selection of multiple spectral variables. Sensors 19: 2898. doi: 10.3390/s19132898
    [86] Hruska Z, Yao H, Kincaid R, et al. (2020) Spectral-based screening approach evaluating two specific maize lines with divergent resistance to invasion by aflatoxigenic fungi. Front Microbiol 10: 3152. doi: 10.3389/fmicb.2019.03152
    [87] Biswas DK, Coulman B, Biligetu B, et al. (2019) Advancing bromegrass breeding through imaging phenotyping and genomic selection: A review. Front Plant Sci 10: 1673. doi: 10.3389/fpls.2019.01673
    [88] Feng X, Zhan Y, Wang Q, et al. (2020) Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping. Plant J 101: 1448-1461. doi: 10.1111/tpj.14597
    [89] Liu B, Li R, Li H, et al. (2019) Crop/Weed discrimination using a field imaging spectrometer system. Sensors 19: 5154. doi: 10.3390/s19235154
    [90] Wang D, Vinson R, Holmes M, et al. (2019) Early Detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Sci Rep 9: 1-14. doi: 10.1038/s41598-018-37186-2
    [91] Nie P, Zhang J, Feng X, et al. (2019) Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sens Actuators B: Chem 296: 126630. doi: 10.1016/j.snb.2019.126630
    [92] Bangelesa F, Adam E, Knight J, et al. (2020) Predicting soil organic carbon content using hyperspectral remote sensing in a degraded mountain landscape in lesotho. Appl Environ Soil Sci 2020: 2158573. doi: 10.1155/2020/2158573
    [93] Chen Y, Wang J, Liu G, et al. (2019) Hyperspectral estimation model of forest soil organic matter in northwest Yunnan Province, China. Forests 10: 217. doi: 10.3390/f10030217
    [94] Guo L, Zhang H, Shi T, et al. (2019) Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma 337: 32-41. doi: 10.1016/j.geoderma.2018.09.003
    [95] Shen Q, Xia K, Zhang S, et al. (2019) Hyperspectral indirect inversion of heavy-metal copper in reclaimed soil of iron ore area. Spectrochim Acta Part A 222: 117191. doi: 10.1016/j.saa.2019.117191
    [96] Zhang S, Shen Q, Nie C, et al. (2019) Hyperspectral inversion of heavy metal content in reclaimed soil from a mining wasteland based on different spectral transformation and modeling methods. Spectrochim Acta Part A 211: 393-400. doi: 10.1016/j.saa.2018.12.032
    [97] Hu J, Peng J, Zhou Y, et al. (2019) Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens 11: 736. doi: 10.3390/rs11070736
    [98] Grafton M, Kaul T, Palmer A, et al. (2019) Regression analysis of proximal hyperspectral data to predict soil pH and Olsen P. Agriculture 9: 55. doi: 10.3390/agriculture9030055
    [99] Patel AK, Ghosh JK (2019) Soil fertility status assessment using hyperspectral remote sensing, Remote sensing for agriculture, Ecosystems, and Hydrology XXI, 111490E.
    [100] Gold KM, Townsend PA, Chlus A, et al. (2020) Hyperspectral measurements enable pre-symptomatic detection and differentiation of contrasting physiological effects of late blight and early blight in potato. Remote Sens 12: 286. doi: 10.3390/rs12020286
    [101] Gorretta N, Nouri M, Herrero A, et al. (2019) Early detection of the fungal disease "apple scab" using SWIR hyperspectral imaging, 2019 10th workshop on hyperspectral imaging and signal processing: Evolution in remote sensing (WHISPERS), 1-4.
    [102] Mahlein AK, Alisaac E, Al Masri A, et al. (2019) Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale. Sensors 19: 2281. doi: 10.3390/s19102281
    [103] Yan Y, Yu W (2019) Early detection of rice blast (pyricularia) at seedling stage based on near-infrared hyper-spectral image, Proceedings of the 2019 8th international conference on bioinformatics and biomedical science, 64-68.
    [104] Gustavsson J, Cederberg C, Sonesson U, et al. (2011) Global food losses and food waste.
    [105] Parfitt J, Barthel M, Macnaughton S (2010) Food waste within food supply chains: Quantification and potential for change to 2050. Philos Trans R Soc, B 365: 3065-3081. doi: 10.1098/rstb.2010.0126
    [106] Liu S, Liu X, Liu M, et al. (2017) Extraction of rice phenological differences under heavy metal stress using EVI time-series from HJ-1A/B Data. Sensors 17: 1243. doi: 10.3390/s17061243
    [107] Xing F, Yao H, Liu Y, et al. (2019) Recent developments and applications of hyperspectral imaging for rapid detection of mycotoxins and mycotoxigenic fungi in food products. Crit Rev Food Sci Nutr 59: 173-180. doi: 10.1080/10408398.2017.1363709
    [108] Liu T, Liu X, Liu M, et al. (2018) Classification of rice heavy metal stress levels based on phenological characteristics using remote sensing time-series images and data mining algorithms. Sensors 18: 4425. doi: 10.3390/s18124425
    [109] Jeong S, Ko J, Yeom J-M (2018) Nationwide projection of rice yield using a crop model integrated with geostationary satellite imagery: a case study in South Korea. Remote Sens 10: 1665. doi: 10.3390/rs10101665
    [110] Lu J, Miao Y, Shi W, et al. (2020) Developing a proximal active canopy sensor-based precision nitrogen management strategy for high-yielding rice. Remote Sens 12: 1440. doi: 10.3390/rs12091440
    [111] Paleari L, Movedi E, Vesely FM, et al. (2019) Estimating crop nutritional status using smart apps to support nitrogen fertilization. A case study on paddy rice. Sensors 19: 981. doi: 10.3390/s19040981
    [112] Park S, Im J, Park S, et al. (2018) Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens 10: 447. doi: 10.3390/rs10030447
    [113] Ranghetti L, Cardarelli E, Boschetti M, et al. (2018) Assessment of water management changes in the Italian rice paddies from 2000 to 2016 using satellite data: A contribution to agro-ecological studies. Remote Sens 10: 416. doi: 10.3390/rs10030416
    [114] Sianturi R, Jetten VG, Ettema J, et al. (2018) Distinguishing between hazardous flooding and non-hazardous agronomic inundation in irrigated rice fields: A case study from west java. Remote Sens 10: 1003. doi: 10.3390/rs10071003
    [115] Zhang S, Li J, Wang S, et al. (2020) Repaid identification and prediction of Cadmium-Lead cross-stress of different stress levels in rice canopy based on visible and near-infrared spectroscopy. Remote Sens 12: 469. doi: 10.3390/rs12030469
    [116] Barnes PW, Searles PS, Ballaré CL, et al. (2000) Non-invasive measurements of leaf epidermal transmittance of UV radiation using chlorophyll fluorescence: field and laboratory studies. Physiol Plant 109: 274-283. doi: 10.1034/j.1399-3054.2000.100308.x
    [117] Du L, Shi S, Yang J, et al. (2016) Using different regression methods to estimate leaf nitrogen content in rice by fusing hyperspectral LiDAR data and laser-induced chlorophyll fluorescence data. Remote Sens 8: 526. doi: 10.3390/rs8060526
    [118] Du L, Yang J, Chen B, et al. (2020) Novel combined spectral indices derived from hyperspectral and laser-induced fluorescence LiDAR spectra for leaf nitrogen contents estimation of rice. Remote Sens 12: 185. doi: 10.3390/rs12010185
    [119] Yang J, Du L, Sun J, et al. (2016) Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra. Opt Express 24: 19354-19365.
    [120] Yang J, Song S, Du L, et al. (2018) Analyzing the effect of fluorescence characteristics on leaf nitrogen concentration estimation. Remote Sens 10: 1402. doi: 10.3390/rs10091402
    [121] Fowler JE (2014) Compressive pushbroom and whiskbroom sensing for hyperspectral remote-sensing imaging, 2014 IEEE international conference on image processing (ICIP), 684-688.
    [122] Kerekes JP, Schott JR (2007) Hyperspectral imaging systems. Hyperspectral data exploitation: Theory and applications, 19-45.
    [123] Iturrino C, Arias FX, Sierra H, et al. (2019) Single-shot multispectral image acquisition for low-altitude remote sensing using light diffraction techniques, 2019 10th workshop on hyperspectral imaging and signal processing: Evolution in remote sensing (WHISPERS), 1-5.
    [124] Blasinski H, Farrell J, Wandell B (2020) Simultaneous surface reflectance and fluorescence spectra estimation. IEEE Trans Image Process 29: 8791-8804. doi: 10.1109/TIP.2020.2973810
    [125] He X, Feng X, Sun D, et al. (2019) Rapid and nondestructive measurement of rice seed vitality of different years using near-infrared hyperspectral imaging. Molecules 24: 2227. doi: 10.3390/molecules24122227
    [126] Yuan J, Su Z, Jia Y, et al. (2016) Identification of rice leaf blast and nitrogen deficiency in cold region using hyperspectral imaging. Trans Chin Soc Agric Eng 32: 155-160.
    [127] Gewali UB, Monteiro ST (2016) A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes, 2016 IEEE international conference on image processing (ICIP), 2216-2220.
    [128] Kampe T, Leisso N, Musinsky J, et al. (2013) The NEON 2013 airborne campaign at domain 17 terrestrial and aquatic sites in California. NEON Technical Memorandum Series, TM-005.
    [129] Ding M, Guan Q, Li L, et al. (2020) Phenology-based rice paddy mapping using multi-source satellite imagery and a fusion algorithm applied to the poyang lake plain, Southern China. Remote Sens 12: 1022. doi: 10.3390/rs12061022
    [130] Du L, Gong W, Shi S, et al. (2016) Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR. Int J Appl Earth Obs Geoinformation 44: 136-143. doi: 10.1016/j.jag.2015.08.008
    [131] Gnyp ML, Miao Y, Yuan F, et al. (2014) Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Res 155: 42-55. doi: 10.1016/j.fcr.2013.09.023
    [132] Cheng T, Song R, Li D, et al. (2017) Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sens 9: 319. doi: 10.3390/rs9040319
    [133] Li D, Wang X, Zheng H, et al. (2018) Estimation of area-and mass-based leaf nitrogen contents of wheat and rice crops from water-removed spectra using continuous wavelet analysis. Plant Methods 14: 76. doi: 10.1186/s13007-018-0344-1
    [134] Liu Z, Shi J, Zhang L, et al. (2010) Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification. J Zhejiang Univ Sci B 11: 71-78. doi: 10.1631/jzus.B0900193
    [135] Zheng H, Ma J, Zhou M, et al. (2020) Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multispectral imagery. Remote Sens 12: 957. doi: 10.3390/rs12060957
    [136] Starr C, Evers C, Starr L (2010) Biology: Concepts and applications, Cengage Learning.
    [137] Das B, Sahoo RN, Biswas A, et al. (2020) Discrimination of rice genotypes using field spectroradiometry. Geocarto Int 35: 64-77. doi: 10.1080/10106049.2018.1506507
    [138] Yang M-D, Huang K-S, Kuo Y-H, et al. (2017) Spatial and spectral hybrid image classification for rice lodging assessment through UAV imagery. Remote Sens 9: 583. doi: 10.3390/rs9060583
    [139] Yang C-M (2010) Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precis Agric 11: 61-81. doi: 10.1007/s11119-009-9122-4
    [140] Dunn BW, Dehaan R, Schmidtke L, et al. (2016) Using field-derived hyperspectral reflectance measurement to identify the essential wavelengths for predicting nitrogen uptake of rice at panicle initiation. J Near Infrared Spectrosc 24: 473-483. doi: 10.1255/jnirs.1246
    [141] Feng H, Jiang N, Huang C, et al. (2013) A hyperspectral imaging system for an accurate prediction of the above-ground biomass of individual rice plants. Rev Sci Instrum 84: 095107. doi: 10.1063/1.4818918
    [142] Kawamura K, Ikeura H, Phongchanmaixay S, et al. (2018) Canopy hyperspectral sensing of paddy fields at the booting stage and PLS regression can assess grain yield. Remote Sens 10: 1249. doi: 10.3390/rs10081249
    [143] Fabiyi SD, Vu H, Tachtatzis C, et al. (2020) Varietal classification of rice seeds using RGB and hyperspectral images. IEEE Access 8: 22493-22505. doi: 10.1109/ACCESS.2020.2969847
    [144] Sun J, Shi S, Yang J, et al. (2018) Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion. Remote Sens Environ 212: 1-7. doi: 10.1016/j.rse.2018.04.024
    [145] Kong W, Zhang C, Liu F, et al. (2013) Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis. Sensors 13: 8916-8927. doi: 10.3390/s130708916
    [146] Devia CA, Rojas JP, Petro E, et al. (2019) High-throughput biomass estimation in rice crops using UAV multispectral imagery. J Intell Rob Syst 96: 573-589. doi: 10.1007/s10846-019-01001-5
    [147] Zhao X, Yuan Y, Song M, et al. (2019) Use of unmanned aerial vehicle imagery and deep learning unet to extract rice lodging. Sensors 19: 3859. doi: 10.3390/s19183859
    [148] Zheng H, Zhou X, Cheng T, et al. (2016) Evaluation of a UAV-based hyperspectral frame camera for monitoring the leaf nitrogen concentration in rice, 2016 IEEE international geoscience and remote sensing symposium (IGARSS), 7350-7353.
    [149] Zhang Y, Gao J, Cen H, et al. (2019) Automated spectral feature extraction from hyperspectral images to differentiate weedy rice and barnyard grass from a rice crop. Comput Electron Agric 159: 42-49. doi: 10.1016/j.compag.2019.02.018
    [150] Witt C, Pasuquin J, Mutters R, et al. (2005) New leaf color chart for effective nitrogen management in rice. Better Crops 89: 36-39.
    [151] Sun J, Shi S, Gong W, et al. (2017) Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer. Sci Rep 7: 40362. doi: 10.1038/srep40362
    [152] Zhou W, Zhang J, Zou M, et al. (2019) Feasibility of using rice leaves hyperspectral data to estimate CaCl 2-extractable concentrations of heavy metals in agricultural soil. Sci Rep 9: 1-9. doi: 10.1038/s41598-018-37186-2
    [153] Uto K, Seki H, Saito G, et al. (2013) Characterization of rice paddies by a UAV-mounted miniature hyperspectral sensor system. IEEE J Sel Top Appl Earth Obs Remote Sens 6: 851-860. doi: 10.1109/JSTARS.2013.2250921
    [154] Inoue Y, Penuelas J (2001) An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications. Int J Remote Sens 22: 3883-3888. doi: 10.1080/01431160110069863
    [155] Li D, Wang R, Xie C, et al. (2020) A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network. Sensors 20: 578. doi: 10.3390/s20030578
    [156] Liu Z, Zhang Q, Han T, et al. (2016) Heavy metal pollution in a soil-rice system in the Yangtze river region of China. Int J Environ Res Pub Health 13: 63. doi: 10.3390/ijerph13010063
    [157] Shwetank JK, Bhatia K (2010) Review of rice crop identification and classification using hyper-spectral image processing system. Int J Comput Sci Commun 1: 253-258.
    [158] Onoyama H, Ryu C, Suguri M, et al. (2018) Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis. Precis Agric 19: 721-734. doi: 10.1007/s11119-017-9552-3
    [159] Nagelkerke NJ, others (1991) A note on a general definition of the coefficient of determination. Biometrika 78: 691-692. doi: 10.1093/biomet/78.3.691
    [160] Samat A, Gamba P, Abuduwaili J, et al. (2016) Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer. Remote Sens 8: 234. doi: 10.3390/rs8030234
    [161] Tehrany MS, Pradhan B, Mansor S, et al. (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125: 91-101. doi: 10.1016/j.catena.2014.10.017
    [162] Liu P, Choo K-KR, Wang L, et al. (2017) SVM or deep learning? A comparative study on remote sensing image classification. Soft Comput 21: 7053-7065. doi: 10.1007/s00500-016-2247-2
    [163] Hecht-Nielsen R (1992) Theory of the backpropagation neural network, Neural networks for perception, Elsevier, 65-93.
    [164] Chen Y, Jiang H, Li C, et al. (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54: 6232-6251. doi: 10.1109/TGRS.2016.2584107
    [165] Wang J, Zhang C, Shi Y, et al. (2020) Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging. Plant Methods 16: 1-16. doi: 10.1186/s13007-019-0534-5
    [166] Thenkabail PS, Enclona EA, Ashton MS, et al. (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91: 354-376. doi: 10.1016/j.rse.2004.03.013
    [167] Chang YL, Chang L, Xu M-X, et al. (2017) Impurity function band prioritization based on particle swarm optimization and gravitational search algorithm for hyperspectral images, 2017 IEEE international geoscience and remote sensing symposium (IGARSS), 1788-1791.
    [168] Knyazikhin Y, Schull MA, Stenberg P, et al. (2013) Hyperspectral remote sensing of foliar nitrogen content. Proc the Natl Acad Sci 110: E185-E192. doi: 10.1073/pnas.1210196109
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