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Abstract
Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.
Highlights
Plant phenotyping involves monitoring of the physicochemical properties of agriculture plants along with their growth while interacting with the surrounding environment [1,2]
This study explores the transfer of models from point spectrometer to mean spectra of spectral images, this was for two main reasons, the first reason was that the open access dataset only has the mean spectra available and the second was that the main interest of plant biologist is to estimate the chemical constituent per plant, which is better represented as a mean value
The results showed that the models made with point spectrometer data can be transferred to the spectral imaging setups by using advanced calibration transfer approaches such as parameter-free calibration enhancement (PFCE) or direct standardization (DS)
Summary
Plant phenotyping involves monitoring of the physicochemical properties of agriculture plants along with their growth while interacting with the surrounding environment [1,2]. Plant breeders commonly practice plant phenotyping to select the best performing plant breeds to develop improved plant varieties dedicated to serving a specific purpose, for example, drought-resistant, salt-tolerant, high-yield, flavor enhancement and many more [1,2,5]. Plant phenotyping for plant breeding is not a new topic and has been practiced since the start of agriculture. In earlier ages, the task was performed by farmers and involves tracking human observable traits such as the size, color and yield of plants. The task can be assumed to be slow and difficult as humans need to follow the plants for their complete growth cycle to assess how they performed at various stages of their life cycle [5]
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