Digital Phenotyping and AI in Plant Breeding: Accelerating Genetic Gains through High-Throughput Data

Author: Vinodh Kumar P.N., Sahana Police Patil and Keerthi G.M.

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Abstract

The integration of digital phenotyping and artificial intelligence (AI) is revolutionizing plant breeding by enabling high-throughput, data-driven selection of superior crop varieties. This review examines how advanced sensing technologies—including drones, LiDAR, and automated phenotyping platforms—coupled with machine learning algorithms are transforming trait measurement, genomic prediction, and stress resilience breeding. While these innovations offer significant advantages, such as reduced breeding cycles and improved prediction accuracy for complex traits, challenges remain in data standardization, computational demands, and field validation. Emerging technologies like AI-driven CRISPR editing, digital twins, and autonomous breeding systems promise to further accelerate genetic gains. The successful implementation of these approaches will require multidisciplinary collaboration and institutional support to overcome existing barriers and fully realize the potential of precision plant breeding

Keywords

digital phenotyping, artificial intelligence, high-throughput phenotyping, genomic selection, precision breeding

Conclusion

The convergence of digital phenotyping and AI technologies is fundamentally transforming plant breeding from an artisanal practice to a data-driven science. While significant challenges remain in data standardization and computational infrastructure recent advancements in edge computing and federated learning are addressing these limitations. The development of specialized AI chips for agricultural applications promises to further reduce processing times and energy consumption. The full realization of these technologies' potential requires unprecedented collaboration across disciplines. Breeders must work closely with computer scientists to develop domain-specific algorithms, while engineers need to design robust field-deployable sensors. Institutional investments in digital infrastructure and workforce training will be critical to bridge the "digital divide" in agricultural research. As these technologies mature, they promise to accelerate genetic gains by 2-3 times current rates while reducing resource inputs. The next decade will likely see the emergence of fully integrated breeding systems where AI drives decision-making from gene discovery to variety release, ushering in a new era of precision plant breeding

References

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How to cite this article

Vinodh Kumar P.N., Sahana Police Patil and Keerthi G.M. (2023). Digital Phenotyping and AI in Plant Breeding: Accelerating Genetic Gains through High-Throughput Data. Biological Forum – An International Journal, 15(10): 1733-1737