Modeling Hyperspectral Response of Water-Stress Induced Lettuce Plants Using Artificial Neural Networks

Modeling the hyperspectral response of vegetables is important for estimating water stress through a noninvasive approach. This article evaluates the hyperspectral response of waterstress induced lettuce (Lactuca sativa L.) using artificial neural networks (ANN). We evenly split 36 lettuce pots into three groups: control, stress, and bacteria. Hyperspectral response was measured four times, during 14 days of stress induction, with an ASD Fieldspec HandHeld spectroradiometer (325–1075 nm). Both reflectance and absorbance measurements were calculated. Different biophysical parameters were also evaluated. The performance of the ANN approach was compared against other machine learning algorithms. Our results show that the ANN approach could separate the water-stressed lettuce from the non-stressed group with up to 80% accuracy at the beginning of the experiment. Additionally, this accuracy improved at the end of the experiment, reaching an accuracy of up to 93%. Absorbance data offered better accuracy than reflectance data to model it. This study demonstrated that it is possible to detect early stages of water stress in lettuce plants with high accuracy based on an ANN approach applied to hyperspectral data. The methodology has the potential to be applied to other species and cultivars in agricultural fields.

[1]  Mariangela Hungria,et al.  Phytohormones and Antibiotics Produced by Bacillus subtilis and their Effects on Seed Pathogenic Fungi and on Soybean Root Development , 2005 .

[2]  Margaret Kalacska,et al.  Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image , 2015 .

[3]  Yoshio Inoue,et al.  Analysis of Airborne Optical and Thermal Imagery for Detection of Water Stress Symptoms , 2018, Remote. Sens..

[4]  Wenxiu Gao,et al.  Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: comparison of partial least-square regression and support vector machine regression methods , 2013 .

[5]  Wenjiang Huang,et al.  Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis , 2018 .

[6]  Abduwasit Ghulam,et al.  Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy , 2017, Remote. Sens..

[7]  Li Lin,et al.  Estimation of Leaf Nitrogen Content in Wheat Using New Hyperspectral Indices and a Random Forest Regression Algorithm , 2018, Remote. Sens..

[8]  Nilton Nobuhiro Imai,et al.  Improvement of leaf nitrogen content inference in Valencia-orange trees applying spectral analysis algorithms in UAV mounted-sensor images , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Subashisa Dutta,et al.  Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery , 2016 .

[10]  Luis Miguel Contreras-Medina,et al.  A Review of Methods for Sensing the Nitrogen Status in Plants: Advantages, Disadvantages and Recent Advances , 2013, Sensors.

[11]  Mehmet Emin Tenekeci,et al.  Detection of pepper fusarium disease using machine learning algorithms based on spectral reflectance , 2020, Sustain. Comput. Informatics Syst..

[12]  W. S. Lee,et al.  DETERMINATION OF SIGNIFICANT WAVELENGTHS AND PREDICTION OF NITROGEN CONTENT FOR CITRUS , 2005 .

[13]  Nitesh K. Poona,et al.  Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning , 2018, Remote. Sens..

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

[15]  Thomas Udelhoven,et al.  Challenges and Future Perspectives of Multi-/Hyperspectral Thermal Infrared Remote Sensing for Crop Water-Stress Detection: A Review , 2019, Remote. Sens..

[16]  Konstantinos P. Ferentinos,et al.  Hyperspectral machine vision as a tool for water stress severity assessment in soilless tomato crop , 2018 .

[17]  Yiannis Ampatzidis,et al.  UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence , 2019, Remote. Sens..

[18]  Yiannis Ampatzidis,et al.  UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning , 2019, Remote. Sens..

[19]  Olubukola Oluranti Babalola,et al.  The influence of plant growth-promoting rhizobacteria in plant tolerance to abiotic stress: a survival strategy , 2018, Applied Microbiology and Biotechnology.

[20]  P. Zarco-Tejada,et al.  Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera , 2012 .

[21]  Kaiyu Guan,et al.  Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms , 2019, Front. Plant Sci..

[22]  Jun Li,et al.  Advanced Spectral Classifiers for Hyperspectral Images: A review , 2017, IEEE Geoscience and Remote Sensing Magazine.

[23]  Pierre Defourny,et al.  Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems , 2018, Remote Sensing of Environment.

[24]  Fan Li,et al.  Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress , 2019, Comput. Electron. Agric..

[25]  Maria de Lourdes Bueno Trindade Galo,et al.  Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Andrew K. Skidmore,et al.  Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits , 2019, Remote Sensing of Environment.

[27]  Jingcheng Zhang,et al.  Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning. , 2019, Pest management science.

[28]  Jia Tian,et al.  Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index , 2019, Remote. Sens..

[29]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[30]  Ruisong Xu,et al.  Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions , 2009, Environmental monitoring and assessment.

[31]  Tauqueer Ahmad,et al.  Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing , 2019, Agricultural Water Management.

[32]  H. Xie,et al.  Modeling alpine grassland forage phosphorus based on hyperspectral remote sensing and a multi-factor machine learning algorithm in the east of Tibetan Plateau, China , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[33]  Ismail M. M. Rahman,et al.  Water Stress in Plants: Causes, Effects and Responses , 2012 .

[34]  M. Rossini,et al.  Inter-comparison of hemispherical conical reflectance factors (HCRF) measured with four fibre-based spectrometers. , 2013, Optics express.