Sustainable biobased packaging via integrated robotics/machine learning workflow for improved postharvest preservation

Reliance on petroleum-derived plastic packaging exacerbates environmental burdens and lacks intrinsic antimicrobial protection against food spoilage. While biobased materials offer a sustainable alternative, optimizing their formulations is hindered by vast component libraries, inefficient trial-and-error experimentation, and complex multiproperty requirements. Herein, we develop an integrated workflow combining robotic automation, machine learning predictions, density functional theory, and life cycle assessment to accelerate biobased packaging discovery. Leveraging data augmentation and ensemble modeling, we use a hierarchical neural network to predict ∼1 billion candidate formulations, balancing mechanical properties, optical transparency, and environmental footprint. The resulting biobased packaging demonstrates enhanced hydrostability, effective antimicrobial activity, and high barrier performance, significantly extending the shelf life of cucumbers and avocados. Last, we release a data-sharing platform featuring forward prediction, inverse design, and environmental impact functions to support community use. This integrative framework provides sustainable, antimicrobial packaging materials alternative to conventional plastic films.