Hyperspectral imaging in crop fields: precision agriculture

Abstract Precision agriculture is starting to play a key role in the new generation of modern agricultural revolution. Farmers are increasingly aware of the importance of maintaining direct control of several fundamental aspects such as the state of health of crops, the amount of water or fertilizer, and possible infections that can develop in the field. Hyperspectral imaging (HSI) and multispectral imaging (MSI) have been applied for these matters for some decades. Nevertheless, there are still several technological and practical barriers to face. This chapter provides an overview of some of the relevant scientific literature related to the application of HSI and MSI on crop fields. Some of the applications are related to the detection of contaminants and heavy metals, the control and assessment of water sources, and the detection of the health status of the fields. The main advantages and constraints and the methods to acquire the images (by using satellites or aerial vehicles) are also shown and discussed.

[1]  R. Wal,et al.  Imagining wildlife: New technologies and animal censuses, maps and museums , 2016 .

[2]  Long Li,et al.  Collaborative inversion heavy metal stress in rice by using two-dimensional spectral feature space based on HJ-1 A HSI and radarsat-2 SAR remote sensing data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Sarah C Dunagan,et al.  Effects of mercury on visible/near-infrared reflectance spectra of mustard spinach plants (Brassica rapa P.). , 2007, Environmental pollution.

[4]  J. Ji,et al.  Can Contaminant Elements in Soils Be Assessed by Remote Sensing Technology: A Case Study With Simulated Data , 2011 .

[5]  G Bonifazi,et al.  Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. , 2010, International journal of food microbiology.

[6]  E. DeLucia,et al.  Indirect Suppression of Photosynthesis on Individual Leaves by Arthropod Herbivory , 2022 .

[7]  Sami F. Masri,et al.  The energy-water agriculture nexus: the past, present and future of holistic resource management via remote sensing technologies , 2016 .

[8]  Fredrik J. Lindgren,et al.  Assessing Biomass and Metal Contents in Riparian Vegetation Along a Pollution Gradient Using an Unmanned Aircraft System , 2014, Water, Air, & Soil Pollution.

[9]  Di Wu,et al.  Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review — Part II: Applications , 2013 .

[10]  Da-Wen Sun,et al.  An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control , 2014 .

[11]  Moon S. Kim,et al.  Development of a Simple Algorithm for the Detection of Chilling Injury in Cucumbers from Visible/Near-Infrared Hyperspectral Imaging , 2005, Applied spectroscopy.

[12]  K. Krezhov,et al.  Detection of environmental changes using hyperspectral remote sensing , 2016 .

[13]  C. D. Rullán-Silva,et al.  Remote Monitoring of Forest Insect Defoliation -A Review- , 2013 .

[14]  P. J. Pinter,et al.  Remote sensing for crop protection , 1993 .

[15]  Gamal ElMasry,et al.  Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging , 2013 .

[16]  Erik Lindholm,et al.  NVIDIA Tesla: A Unified Graphics and Computing Architecture , 2008, IEEE Micro.

[17]  Yong He,et al.  Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing , 2014 .

[18]  L. Plümer,et al.  Detection of early plant stress responses in hyperspectral images , 2014 .

[19]  Paul E. Gessler,et al.  Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments , 2008 .

[20]  José Ramón Rodríguez-Pérez,et al.  Using field spectrometry and a plant probe accessory to determine leaf water content in commercial vineyards , 2015 .

[21]  U. Schmidhalter,et al.  Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars , 2015 .

[22]  Nuria Aleixos,et al.  Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.

[23]  Antonio J. Plaza,et al.  Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data , 2007, The Journal of Supercomputing.

[24]  Heather McNairn,et al.  International Journal of Applied Earth Observation and Geoinformation , 2014 .

[25]  Grzegorz Siebielec,et al.  Near- and mid-infrared diffuse reflectance spectroscopy for measuring soil metal content. , 2004, Journal of environmental quality.

[26]  Antonio J. Plaza,et al.  Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery , 2016, Remote. Sens..

[27]  Xiuqin Rao,et al.  Detection of common defects on oranges using hyperspectral reflectance imaging , 2011 .

[28]  M. Schildhauer,et al.  Monitoring plant functional diversity from space , 2016, Nature Plants.

[29]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[30]  Liangpei Zhang,et al.  Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Louise McRae,et al.  Global biodiversity monitoring: From data sources to Essential Biodiversity Variables , 2017 .

[32]  Colm P. O'Donnell,et al.  Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms , 2011 .

[33]  Larry D. Godfrey,et al.  Remote Sensing for Detection of Cotton Aphid– (Homoptera: Aphididae) and Spider Mite– (Acari: Tetranychidae) Infested Cotton in the San Joaquin Valley , 2006 .

[34]  L. Kumar,et al.  A review of data assimilation of remote sensing and crop models , 2018 .

[35]  I. Savin,et al.  Near infrared spectroscopy techniques for soil contamination assessment in the Nile Delta , 2016, Eurasian Soil Science.

[36]  R. Clark,et al.  Spectroscopic Determination of Leaf Biochemistry Using Band-Depth Analysis of Absorption Features and Stepwise Multiple Linear Regression , 1999 .

[37]  Y. E. Yildirim,et al.  Spectral reflectance characteristics of sugar beet under different levels of irrigation water and relationships between growth parameters and spectral indexes , 2011 .

[38]  Hao Zhang,et al.  Land use pattern, socio-economic development, and assessment of their impacts on ecosystem service value: study on natural wetlands distribution area (NWDA) in Fuzhou city, southeastern China , 2013, Environmental Monitoring and Assessment.

[39]  A. Skidmore,et al.  Red edge shift and biochemical content in grass canopies , 2007 .

[40]  A. Mueller,et al.  Spectral identification of pyrite mud weathering products: a field and laboratory evaluation , 2011 .

[41]  James W. Jones,et al.  Uncertainty in Simulating Wheat Yields Under Climate Change , 2013 .

[42]  Lin Li,et al.  Estimation of heavy-metal contamination in soil using reflectance spectroscopy and partial least-squares regression , 2010 .

[43]  Liangpei Zhang,et al.  Sparsity-Regularized Robust Non-Negative Matrix Factorization for Hyperspectral Unmixing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  Jie Li,et al.  Image super-resolution: The techniques, applications, and future , 2016, Signal Process..

[45]  Karsten Schulz,et al.  Retrieval of Leaf Area Index (LAI) and Soil Water Content (WC) Using Hyperspectral Remote Sensing under Controlled Glass House Conditions for Spring Barley and Sugar Beet , 2010, Remote. Sens..

[46]  Meiling Liu,et al.  Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices , 2010 .

[47]  Tom Royer,et al.  Airborne Multi-Spectral Remote Sensing of Russian Wheat Aphid1 Injury to Wheat , 2007 .

[48]  Anton J. J. van Rompaey,et al.  he effect of atmospheric and topographic correction methods on land cover lassification accuracy , 2013 .

[49]  Christian Nansen,et al.  Remote Sensing and Reflectance Profiling in Entomology. , 2016, Annual review of entomology.

[50]  M. Ngadi,et al.  Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .

[51]  Stefan Sommer,et al.  Use of airborne hyperspectral data to estimate residual heavy metal contamination and acidification potential in the Guadiamar floodplain Andalusia, Spain after the Aznacollar mining accident , 2004, SPIE Remote Sensing.

[52]  Bart Nicolai,et al.  Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging , 2011 .

[53]  Ruiliang Pu,et al.  Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index , 2003, IEEE Trans. Geosci. Remote. Sens..

[54]  He Li,et al.  Comparative analysis of GF-1, HJ-1, and Landsat-8 data for estimating the leaf area index of winter wheat , 2017 .

[55]  Moon S. Kim,et al.  A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves , 2014 .

[56]  Moon S. Kim,et al.  Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery , 2013 .

[57]  Arnold Bregt,et al.  Geosensors to Support Crop Production: Current Applications and User Requirements , 2011, Sensors.

[58]  Antonio J. Plaza,et al.  An experimental comparison of parallel algorithms for hyperspectral analysis using heterogeneous and homogeneous networks of workstations , 2008, Parallel Comput..

[59]  Moon S. Kim,et al.  Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .

[60]  Yew-Soon Ong,et al.  Multifactorial Evolution: Toward Evolutionary Multitasking , 2016, IEEE Transactions on Evolutionary Computation.

[61]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[62]  Roberta E. Martin,et al.  Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing , 2016 .

[63]  Jan G. P. W. Clevers,et al.  Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[64]  D. Moshou,et al.  The potential of optical canopy measurement for targeted control of field crop diseases. , 2003, Annual review of phytopathology.

[65]  Armando Apan,et al.  Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery , 2004 .

[66]  Chein-I Chang,et al.  A Review of Unsupervised Spectral Target Analysis for Hyperspectral Imagery , 2010, EURASIP J. Adv. Signal Process..

[67]  N. Robinson,et al.  Advances in precision agriculture in south-eastern Australia. II. Spatio-temporal prediction of crop yield using terrain derivatives and proximally sensed data , 2009 .

[68]  J. Townshend,et al.  Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing , 2001 .

[69]  Noel D.G. White,et al.  Classification of Fungal Infected Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine , 2007 .

[70]  Xuan Zhu,et al.  Sowing date detection at the field scale using CubeSats remote sensing , 2019, Comput. Electron. Agric..

[71]  Anming Bao,et al.  Estimation of leaf water content in cotton by means of hyperspectral indices , 2013 .

[72]  R. Baker,et al.  The role of climatic mapping in predicting the potential geographical distribution of non-indigenous pests under current and future climates , 2000 .

[73]  Brian Fenton,et al.  Plant responses to insect herbivory: interactions between photosynthesis, reactive oxygen species and hormonal signalling pathways. , 2012, Plant, cell & environment.

[74]  Zoltán Barcza,et al.  Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices , 2018, Agricultural and Forest Meteorology.

[75]  M. Stone,et al.  Estimating Water Stress in Plants Using Hyperspectral Sensing , 2004 .

[76]  Junhong Liu,et al.  Single-Kernel Maize Analysis by Near-Infrared Hyperspectral Imaging , 2004 .

[77]  Antonio J. Plaza,et al.  A New Genetic Method for Subpixel Mapping Using Hyperspectral Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[78]  Gregory P. Asner,et al.  Integrating technologies for scalable ecology and conservation , 2016 .

[79]  Walter E. Riedell,et al.  Leaf Reflectance Spectra of Cereal Aphid-Damaged Wheat , 1999 .

[80]  W. Turner.  Sensing biodiversity , 2014, Science.

[81]  Kyoung-Woong Kim,et al.  Qualitative analysis and mapping of heavy metals in an abandoned Au–Ag mine area using NIR spectroscopy , 2009 .

[82]  Jianwei Qin,et al.  DETECTION OF PITS IN TART CHERRIES BY HYPERSPECTRAL TRANSMISSION IMAGING , 2005 .

[83]  N. Elliott,et al.  Differentiating stress to wheat fields induced by Diuraphis noxia from other stress causing factors , 2013 .

[84]  Jiewen Zhao,et al.  Detection of Bruise on Pear by Hyperspectral Imaging Sensor with Different Classification Algorithms , 2010 .

[85]  James W. Jones,et al.  How do various maize crop models vary in their responses to climate change factors? , 2014, Global change biology.

[86]  Scott Hauck,et al.  The roles of FPGAs in reprogrammable systems , 1998, Proc. IEEE.

[87]  Hongbin Pu,et al.  Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging , 2014 .

[88]  Peter H. Verburg,et al.  Mapping ecosystem services: The supply and demand of flood regulation services in Europe , 2014 .

[89]  Diane S Lidke,et al.  Hyperspectral Confocal Fluorescence Imaging: Exploring Alternative Multivariate Curve Resolution Approaches , 2009, Applied spectroscopy.

[90]  J R Riley,et al.  REMOTE SENSING IN ENTOMOLOGY , 1989 .

[91]  Frank Ewert,et al.  Crop modelling for integrated assessment of risk to food production from climate change , 2015, Environ. Model. Softw..

[92]  Yunseop Kim,et al.  Hyperspectral Image Analysis for Plant Stress Detection , 2010 .

[93]  Antonio J. Plaza,et al.  Commodity cluster-based parallel processing of hyperspectral imagery , 2006, J. Parallel Distributed Comput..

[94]  G. Benelli,et al.  Protecting crop species from biotic and abiotic constraints in the era of Global Change: are we ready for this challenge? , 2016 .

[95]  Stephen J. Symons,et al.  Using a Short Wavelength Infrared (SWIR) hyperspectral imaging system to predict alpha amylase activity in individual Canadian western wheat kernels , 2009 .

[96]  Stefan Pätzold,et al.  Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity , 2008 .

[97]  Christian Nansen,et al.  Agricultural Case Studies of Classification Accuracy, Spectral Resolution, and Model Over-Fitting , 2013, Applied spectroscopy.

[98]  B. Kuswandi,et al.  Nanosensors for the Detection of Food Contaminants , 2017 .

[99]  R. Sahoo,et al.  Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices , 2014 .

[100]  Cristina E. Davis,et al.  Advanced methods of plant disease detection. A review , 2014, Agronomy for Sustainable Development.

[101]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[102]  George P. Petropoulos,et al.  Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping , 2012, Expert Syst. Appl..

[103]  Alexey L. Lastovetsky,et al.  High Performance Heterogeneous Computing , 2009, Wiley series on parallel and distributed computing.

[104]  Liangpei Zhang,et al.  Semisupervised Subspace-Based DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[105]  Antonio J. Plaza,et al.  Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[106]  José Manuel Amigo,et al.  Hyperspectral image analysis. A tutorial. , 2015, Analytica chimica acta.

[107]  Saed Sayad.  Real Time Data Mining , 2011 .

[108]  Peijun Du,et al.  Estimation of Arsenic Contamination in Reclaimed Agricultural Soils Using Reflectance Spectroscopy and ANFIS Model , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[109]  Chein-I Chang,et al.  High Performance Computing in Remote Sensing , 2007, HiPC 2007.

[110]  Zexuan Zhu,et al.  Computational intelligence in optical remote sensing image processing , 2018, Appl. Soft Comput..

[111]  Karsten Rebner,et al.  Hyperspectral Imaging: A Review of Best Practice, Performance and Pitfalls for in-line and on-line Applications , 2012 .

[112]  Norman C. Elliott,et al.  Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors , 2011 .

[113]  J. Trumble,et al.  Plant compensation for arthropod herbivory , 1993 .

[114]  Freek D. van der Meer,et al.  Mapping of heavy metal pollution in stream sediments using combined geochemistry, field spectroscopy, and hyperspectral remote sensing: A case study of the Rodalquilar mining area, SE Spain , 2008 .

[115]  Jan G. P. W. Clevers,et al.  Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop , 2017, Remote. Sens..

[116]  Antonietta Baiano,et al.  Application of hyperspectral imaging for prediction of physico-chemical and sensory characteristics of table grapes , 2012 .

[117]  Norman C. Elliott,et al.  Original paper: Development of a method using multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused by Diuraphis noxia , 2011 .

[118]  Renfu Lu,et al.  Prediction of firmness and soluble solids content of blueberries using hyperspectral reflectance imaging , 2013 .