Machine learning (ML) is rapidly reshaping plant biotechnology, yet current literature remains fragmented across subfields and often emphasizes applications without critically examining a systemic integration into agricultural workflows (methodological trade-offs or practical deployment constraints). Thus, this review critically explores the ML applications across major domains of plant biotechnology, including biopharmaceutical production, genetic engineering and proteomics, plant phenomics and phenotyping, and plant tissue culture, using a comparative framework. The review highlights that across domains, the model selection is largely governed by data structure. Hence, deep learning algorithms dominate image-based diagnostics, while ensemble models remain common in. multivariate bioprocess and tissue culture. The successful integration of ML in plant biotechnology is contingent upon model selection, performance evaluation, applicability in real-world scenarios, and deployment strategies. By understanding and addressing these interconnected dynamics, stakeholders can harness the full potential of ML technologies to transform plant biotechnology. While ML presents exciting opportunities in plant biotechnology, pertinent issues like enhancing data availability, establishing standardized datasets, ensuring model transferability, and facilitating integration into applied workflows are vital for realizing the full potential of ML in this field. By addressing these challenges through targeted research, collaboration, and the development of best practices, various sectors can significantly benefit from the advancements offered by ML technologies integration in plant biotechnology.
Ogra et al. (Tue,) studied this question.