Common Latent Space Exploration for Calibration Transfer across Hyperspectral Imaging-Based Phenotyping Systems

Abstract

Hyperspectral imaging has increasingly been used in high-throughput plant phenotyping systems. Rapid advancement in the field of phenotyping has resulted in a wide array of hyperspectral imaging systems. However, sharing the plant feature prediction models between different phenotyping facilities becomes challenging due to the differences in imaging environments and imaging sensors. Calibration transfer between imaging facilities is crucially important to cope with such changes. Spectral space adjustment methods including direct standardization (DS), its variants (PDS, DPDS) and spectral scale transformation (SST) require the standard samples to be imaged in different facilities. However, in real-world scenarios, imaging the standard samples is practically unattractive. Therefore, in this study, we presented three methods (TCA, c-PCA, and di-PLSR) to transfer the calibration models without requiring the standard samples. In order to compare the performance of proposed approaches, maize plants were imaged in two greenhouse-based HTPP systems using two pushbroom-style hyperspectral cameras covering the visible near-infrared range. We tested the proposed methods to transfer nitrogen content (N) and relative water content (RWC) calibration models. The results showed that prediction R2 increased by up to 14.50% and 42.20%, while the reduction in RMSEv was up to 74.49% and 76.72% for RWC and N, respectively. The di-PLSR achieved the best results for almost all the datasets included in this study, with TCA being second. The performance of c-PCA was not at par with the di-PLSR and TCA. Our results showed that the di-PLSR helped to recover the performance of RWC, and N models plummeted due to the differences originating from new imaging systems (sensor type, spectrograph, lens system, spatial resolution, spectral resolution, field of view, bit-depth, frame rate, and exposure time) or lighting conditions. The proposed approaches can alleviate the requirement of developing a new calibration model for a new phenotyping facility or to resort to the spectral space adjustment using the standard samples.

Non-destructive testing of mechanical properties of solid wood panel based on partial least squares structural equation modeling transfer method

“…Factor analysis (FA) is a highly effective method for establishing relationships between two sets of measurements (spectrum of two instruments). Transfer methods based on factor analysis, such as the Spectral Space Transformation algorithm (SST) (L. Li et al 2022;Du et al 2011), the alternating trilinear decomposition (ATLD) algorithm (Yap et al 2022), the Principal Component Analysis (PCA) algorithm (Rehman et al 2022), and the Canonical Correlation Analysis (CCA) algorithm (Fan et al 2008;Zheng et al 2014), have been widely applied and are frequently compared to traditional methods. FA, in contrast to PCR or PLS (Mendoza et al 2018), utilizes correlation rather than covariance.…”