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Algorithmically-guided postharvest protocols by experimental combinatorial optimization

The application of search and learning to experimental domains, where the objective function cannot be accurately simulated, but rather requires a measurement in real industrial settings, lies in the focus of this study. We consider the problem of devising treatment protocols for fresh cucumbers, whose quality rapidly deteriorates once being harvested, by considering the combinatorial space of possible postharvest practices. The overall target is to prescribe a combination of treatments, with specified activation levels, that minimizes the cucumbers' quality loss after 4 weeks in two storage environments: 10°C and 20°C. This study engaged with a postharvest laboratory with industrial settings to research and develop a sequential experimentation procedure, in a closed feedback-loop fashion, and subject to strict budget and timeline constraints. The laboratory measurements comprise the assay of color, stiffness and mass, as well as external blemishes - in both harvest and post-4-weeks points in time. Their deviations constitute the aggregated objective function that undergoes minimization for both temperatures. After formulating the optimization problem, we outline our approach and report on the attained results. The obtained protocols significantly outperform the best-known human reference practice, and their nature is visualized and analyzed. Finally, we mention the impact and outlook for industry.