Calibration transfer across multiple hyperspectral imaging-based plant phenotyping systems: I - Spectral space adjustment

Abstract Hyperspectral Imaging is one of the most popular technologies in plant phenotyping. Various kinds of hyperspectral imaging systems have been developed in the past years. However, there are always significant differences in sensors and imaging environmental conditions between different facilities, which makes it difficult to share image processing algorithms and plant features prediction models. Calibration transfer between the imaging systems is critically important. The white referencing calibration has been proved effective in removing the major lighting intensity differences, but significant spectral differences between systems still exist. In this study, we considered the calibration transfer as a spectral adjustment process and compared four different methods (DS, PDS, DPDS and SST) for adjusting the spectra. To perform the calibration transfer, maize plants were imaged using push-broom style, top-view VNIR hyperspectral camera in two greenhouse phenotyping facilities (inter-facility) and within the same facility (intra-facility) under different imaging conditions. The suggested spectral adjustment methods were tested using master partial least squares regression (PLSR) based calibration models developed for predicting the relative water content (RWC) and nitrogen content (N) of maize plants. Results showed that spectral space transformation (SST) decreased the RMSEv from 9.450% and 10.636% to 3.280% and 2.424% for the predicted RWC of intra and inter-facility transfer, respectively, while the corresponding measures achieved by direct standardization (DS) were 3.412% and 3.105%, respectively. In case of N predictions, similar results were observed for DS and SST. The other two methods (PDS and DPDS) reduced the prediction error for RWC and N, but their performance was not on par with DS and SST. The results indicated that the proposed methods, especially DS and SST were able to alleviate the perturbations inherited in the spectra and thus can help to avoid the need for time-consuming, labor-intensive and costly full recalibration process that arose due to the intra or inter-facility variations.

[1]  Xueguang Shao,et al.  Correcting Multivariate Calibration Model for near Infrared Spectral Analysis without Using Standard Samples , 2015 .

[2]  Zhou Shi,et al.  Improved estimates of organic carbon using proximally sensed vis–NIR spectra corrected by piecewise direct standardization , 2015 .

[3]  Alberto Ferrer,et al.  Calibration transfer between NIR spectrometers: New proposals and a comparative study , 2017 .

[4]  Xinge You,et al.  Robust calibration model transfer , 2016 .

[5]  T. Fearn.  Standardisation and Calibration Transfer for near Infrared Instruments: A Review , 2001 .

[6]  Shahryar F. Kianian,et al.  A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging , 2018, Front. Plant Sci..

[7]  Jan Gerretzen,et al.  Simple and Effective Way for Data Preprocessing Selection Based on Design of Experiments. , 2015, Analytical chemistry.

[8]  Yan Liu,et al.  Study on the Calibration Transfer of Soil Nutrient Concentration from the Hyperspectral Camera to the Normal Spectrometer , 2020 .

[9]  Sildomar T. Monteiro,et al.  Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at $\sim$900 nm as an Indicator of Mineralogy , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Jerry Workman,et al.  A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy , 2018, Applied spectroscopy.

[11]  Yong He,et al.  Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds , 2012, Sensors.

[12]  Wenqian Huang,et al.  Long-term evaluation of soluble solids content of apples with biological variability by using near-infrared spectroscopy and calibration transfer method , 2019, Postharvest Biology and Technology.

[13]  E. Moltó,et al.  Calibration transfer between NIR diode array and FT-NIR spectrophotometers for measuring the soluble solids contents of apple , 2007 .

[14]  U. Schurr,et al.  Plant Phenotyping: Past, Present, and Future , 2019, Plant phenomics.

[15]  Christian W Huck,et al.  Comparison of Multivariate Regression Models Based on Water- and Carbohydrate-Related Spectral Regions in the Near-Infrared for Aqueous Solutions of Glucose , 2019, Molecules.

[16]  Ruoqiu Zhang,et al.  Calibration transfer based on the weight matrix (CTWM) of PLS for near infrared (NIR) spectral analysis , 2018 .

[17]  Yufeng Ge,et al.  High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel , 2019, Plant Methods.

[18]  Christiaan van der Tol,et al.  Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion , 2016, Remote. Sens..

[19]  Xin Li,et al.  Calibration transfer of near-infrared spectroscopy by canonical correlation analysis coupled with wavelet transform. , 2017, The Analyst.

[20]  F. Loreto,et al.  Plant Phenotyping Research Trends, a Science Mapping Approach , 2019, Front. Plant Sci..

[21]  Jian Jin,et al.  Automated leaf movement tracking in time-lapse imaging for plant phenotyping , 2020, Comput. Electron. Agric..

[22]  Yufeng Ge,et al.  High Throughput In vivo Analysis of Plant Leaf Chemical Properties Using Hyperspectral Imaging , 2017, Front. Plant Sci..

[23]  Tanzeel U. Rehman,et al.  Optimized angles of the swing hyperspectral imaging system for single corn plant , 2019, Comput. Electron. Agric..

[24]  J. A. Fernández Pierna,et al.  Standardization of milk mid-infrared spectra from a European dairy network. , 2015, Journal of dairy science.

[25]  Dong Hwan Kim,et al.  An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis , 2018, PloS one.

[26]  Joachim Hill,et al.  Preprocessing Ground-Based Visible/Near Infrared Imaging Spectroscopy Data Affected by Smile Effects , 2019, Sensors.

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

[28]  Cecilia Riccioli,et al.  Calibration Transfer from Micro NIR Spectrometer to Hyperspectral Imaging: a Case Study on Predicting Soluble Solids Content of Bananito Fruit (Musa acuminata) , 2018, Food Analytical Methods.

[29]  Ashok Samal,et al.  Leveraging Image Analysis for High-Throughput Plant Phenotyping , 2019, Front. Plant Sci..

[30]  Jian Jin,et al.  Removal of greenhouse microclimate heterogeneity with conveyor system for indoor phenotyping , 2019, Comput. Electron. Agric..

[31]  A. Walter,et al.  Plant phenotyping: from bean weighing to image analysis , 2015, Plant Methods.

[32]  Haiyan Ji,et al.  NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface , 2019, Sensors.

[33]  Alisa Rudnitskaya,et al.  Calibration Update and Drift Correction for Electronic Noses and Tongues , 2018, Front. Chem..

[34]  R. Yu,et al.  Maintaining the predictive abilities of multivariate calibration models by spectral space transformation. , 2011, Analytica chimica acta.