Deep adversarial domain adaptation for hyperspectral calibration model transfer among plant phenotyping systems

Abstract

Hyperspectral imaging has widely been used for plant phenotyping applications. Calibration transfer is crucial for eliminating the prediction drift when a model developed in one plant phenotyping system needs to be deployed in another system. In this study two approaches (Domain Adversarial Neural Network - DANN and Adversarial Discriminative Domain Adaptation – ADDA) for transferring a deep learning model across hyperspectral imaging-based plant phenotyping systems were presented. The proposed techniques employ adversarial learning to minimise the discrepancy between the spectral data of two imaging facilities in the feature space. The calibration transfer analysis was examined by collecting the hyperspectral images of maize plants in two phenotyping facilities equipped with different sensors covering the visible near-infrared (VNIR) region under different lighting conditions. The capability of the suggested methods to transfer a relative water content (RWC) prediction model was investigated. The results showed that the prediction R 2 improved from 0.603 to 0.849 and 0.843 and RMSE reduced from 23.52% to 14.15% and 12.73% for DANN and ADDA, respectively. The study showed that both approaches improved the performance of the transferred model compared to a similar deep learning model without adversarial transfer. Visualisation of feature space showed that both methods lead to similar source and target feature distributions, thus indicating that a prediction model developed using these common features can successfully be applied to both imaging facilities. The main benefit of the DANN and ADDA is that they do not need the standard samples to be imaged in two phenotyping systems or the collection of labelled data to carry out the transfer. The adversarial learning-based calibration transfer can promote the sharing of prediction models across different phenotyping systems. • Differences across phenotyping facilities hinders the application of exiting models. • Calibration transfer between different phenotyping facilities is critically important. • Adversarial domain adaptation based methods were proposed for calibration transfer. • Proposed methods resulted in RWC model shared between two facilities. • Proposed methods can help to eliminate the need of full recalibration.