Hyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping

Abstract

The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non-destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis-related parameters, which require laborious, time-consuming measurements, were also investigated. Traditional laboratory-based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state-of-the-art performance of the deep-learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high-throughput phenotyping for studying plant salinity stress using a combination of HSI and deep-learning approaches.

Plant phenotyping relevance

HSIと深層学習による植物・葉のセグメンテーションおよび生理形質推定が研究の中心であり、高スループット表現型取得手法を実装・評価している。

Code and data availability

The authors publicly deposited the plant/leaf segmentation models in CodeOcean and the MMD clustering source code on GitHub, both directly supporting this paper's phenotyping analysis. The CVPPP 2015 dataset and Hitachi annotation tool are third-party/generic resources, not paper-specific assets.