PLANT PHENOTYPING MARKET ANALYSIS

Market Concentration and Competitive Landscape

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Growing application of AI and machine learning

Another driver propelling the plant phenotyping market forward is the increasing application of artificial intelligence and machine learning technologies. While early automated phenotyping platforms focused on image capture and trait scoring, the focus is now shifting to deploying advanced analytics for extracting even more actionable insights from the vast volumes of phenotypic data. Computer vision and deep learning algorithms are being employed for tasks like comprehending whole-plant architectural features, automating image segmentation and classification, predictive analysis of genotype-phenotype relationships. This helps overcome the bottleneck of manually analyzing petabytes of imagery and phenotype readings generated each season.

AI brings the promise of accelerating discovery from phenomics research manifolds. It allows parsing complex patterns, connecting phenotypes to genotypic or environmental factors that may not be visually evident. Machine learning models can be developed to predict plant performance based on morphological traits observed at seedling stage itself. Researchers are exploring using phenomics and AI together to generate virtual phenotypes under different environmental conditions in silico, before proceeding to costly and time-consuming field trials. As the power and scope of applying automation, predictive analytics and modeling to plant phenotyping expands, it is significantly boosting the potential value it delivers. Many solution providers have started incorporating AI/ML capabilities into their product portfolio, which is expected to make phenotyping an increasingly digitally driven research domain.