Optimized angles of the swing hyperspectral imaging system for single corn plant

Abstract During recent years, hyperspectral imaging systems have been widely applied in the greenhouses for plant phenotyping purposes. Current systems are typically designed as either top view or side view imaging mode. Top view is an ideal imaging angle for top leaves with flat leaf surfaces. However, most bottom leaves are either blocked or shaded. From side view, the entire plant structure is viewable. However, most leaf surfaces are not facing the camera, which impacts measurement quality. Besides, there could be advantages with certain tilted angle(s) between top view and side view. It’s interesting to explore the impact of different imaging angles to the phenotyping quality. For this purpose, a swing hyperspectral imaging system capable of capturing images at any angle from side view (0°) to top view (90°) by rotating the camera and the lighting source was designed. Corn plants were grown and allocated into 3 different treatments: high nitrogen (N) and well-watered (control), high N and drought-stressed, and low N and well-watered. Each plant was imaged at 7 different angles from 0° to 90° with an interval of 15°. The soil plant analysis development (SPAD) values and relative water content (RWC) ground truth measurements were used to establish treatment effects. The results showed that averaged plant-level Normalized Difference Vegetation Index (NDVI) values of plants in different treatments changed at different imaging angles. The results also indicated that for pixel-level NDVI distributions, the titled imaging angle of 75° was optimal to distinguish different water treatments, whereas, the tilted imaging angle of 15° was optimal to distinguish different N treatments. For pixel-level RWC distributions, the distribution difference between different water treatments was larger at higher imaging angles.

[1]  Ulrich Schurr,et al.  Future scenarios for plant phenotyping. , 2013, Annual review of plant biology.

[2]  P. Langridge,et al.  Breeding Technologies to Increase Crop Production in a Changing World , 2010, Science.

[3]  W. Collins,et al.  Remote sensing of crop type and maturity , 1978 .

[4]  Philip N. Slater,et al.  Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres , 1983 .

[5]  Yong He,et al.  Hyperspectral Imaging Coupled with Random Frog and Calibration Models for Assessment of Total Soluble Solids in Mulberries , 2015, Journal of analytical methods in chemistry.

[6]  T. C. Hsiao,et al.  Interactive Water and Nitrogen Effects on Senescence of Maize. II. Photosynthetic Decline and Longevity of Individual Leaves , 1988 .

[7]  Sung-Hyuk Cha,et al.  On measuring the distance between histograms , 2002, Pattern Recognit..

[8]  Stephen M. Welch,et al.  Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area , 2016, Comput. Electron. Agric..

[9]  Simon Bennertz,et al.  Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection , 2018, Sensors.

[10]  Christian Klukas,et al.  Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping1[C][W][OPEN] , 2014, Plant Physiology.

[11]  Jinha Jung,et al.  A ground based platform for high throughput phenotyping , 2017, Comput. Electron. Agric..

[12]  Qin Zhang,et al.  A Review of Imaging Techniques for Plant Phenotyping , 2014, Sensors.

[13]  Neil C. Turner,et al.  Techniques and experimental approaches for the measurement of plant water status , 1981, Plant and Soil.

[14]  C. Elvidge,et al.  Comparison of broad-band and narrow-band red and near-infrared vegetation indices , 1995 .

[15]  M. A. Matin,et al.  Leaf water potential, relative water content, and diffusive resistance as screening techniques for drought resistance in barley , 1989 .

[16]  Christian Correa,et al.  Reliability of different color spaces to estimate nitrogen SPAD values in maize , 2017, Comput. Electron. Agric..

[17]  M. Liu,et al.  A high-throughput stereo-imaging system for quantifying rape leaf traits during the seedling stage , 2017, Plant Methods.

[18]  Age K. Smilde,et al.  UvA-DARE ( Digital Academic Repository ) Assessment of PLSDA cross validation , 2008 .

[19]  Fei Liu,et al.  Hyperspectral Imaging for Mapping of Total Nitrogen Spatial Distribution in Pepper Plant , 2014, PloS one.

[20]  Francois Tardieu,et al.  Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions. , 2007, Plant, cell & environment.

[21]  M. Sumner,et al.  Application of Beaufils' diagnostic indices to maize data published in the literature irrespective of age and conditions , 1977, Plant and Soil.

[22]  H. Scharr,et al.  GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. , 2012, Functional plant biology : FPB.

[23]  Malia A. Gehan,et al.  Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. , 2015, Current opinion in plant biology.

[24]  M. M. Christ,et al.  Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. , 2007, The New phytologist.

[25]  C. Yin,et al.  Differences in some morphological, physiological, and biochemical responses to drought stress in two contrasting populations of Populus przewalskii , 2006 .

[26]  D. Inzé,et al.  Cell to whole-plant phenotyping: the best is yet to come. , 2013, Trends in plant science.

[27]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[28]  Geoffrey M. Henebry,et al.  A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method , 2013, Remote. Sens..

[29]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[30]  Falk Schreiber,et al.  HTPheno: An image analysis pipeline for high-throughput plant phenotyping , 2011, BMC Bioinformatics.

[31]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[32]  G. N. Tiwari,et al.  Temperature distribution in different zones of the micro-climate of a greenhouse: a dynamic model , 1999 .

[33]  K. Chenu,et al.  PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. , 2006, The New phytologist.

[34]  Humberto Bustince,et al.  New method to assess barley nitrogen nutrition status based on image colour analysis , 2009 .

[35]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[36]  Jörg Peter Baresel,et al.  Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat , 2017, Comput. Electron. Agric..