High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image.

Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants' growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.

Review: Application of Artificial Intelligence in Phenomics.

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

Enhancement of isoflavone aglycone, amino acid, and CLA contents in fermented soybean yogurts using different strains: Screening of antioxidant and digestive enzyme inhibition properties.

This study was the first to evaluate changes in isoflavone, amino acid, conjugated linoleic acid (CLA), antioxidant effect, and digestive enzyme inhibition during fermentation of soy-milk to soy-yogurt with L. brevis and L. plantarum. Total average isoflavones were reduced (1318.2 â†’ 971.1 µg/g) with an increase of aglycones (60.2 â†’ 804.9 µg/g, genistein > daidzein > glycitein) in soy powder yogurts (SPYs). Amino acids increased considerably, as did ornithine (average 4.1 â†’ 551.0 mg/g), and CLA showed high variations from not-detected (ND) to 0.5, 0.9 mg/g (cis-9, trans-11) and ND to 0.3, 0.2 mg/g (trans-10, cis-12). Digestive enzyme inhibitions (α-glucosidase, α-amylase, and pancreatic lipase) displayed high activities (average 50.6 â†’ 67.2, 5.2 â†’ 46.4, 10.6 â†’ 51.4%). Moreover, the antioxidant abilities against radicals were elevated as follows: ABTS > DPPH > hydroxyl (average 63.5 â†’ 86.5, 50.2 â†’ 70.3, 39.3 â†’ 55.2%). Specifically, SPY using mixed strains exhibited the greatest enzymatic inhibition and antioxidant capacities.