The evolution of colour vision is captivating, as it reveals the adaptive strategies of extinct species while simultaneously inspiring innovations in modern imaging technology. In this study, we present a simplified model of visual transduction in the retina, introducing a novel opsin layer. We quantify evolutionary pressures by measuring machine vision recognition accuracy on colour images shaped by specific opsins. Building on this, we develop an evolutionary conservation optimisation algorithm to reconstruct the spectral sensitivity of opsins, enabling mutation-driven adaptations to more effectively spot fruits or predators. This model condenses millions of years of evolution into seconds using GPU, providing an experimental framework to test long-standing hypotheses in evolutionary biology, such as vision of early mammals, primate trichromacy from gene duplication, retention of colour blindness, blue-shift of fish rod and multiple rod opsins with bioluminescence. Beyond validating existing paleontological hypotheses, our model further enables speculative explorations of hypothetical species, such as organisms with visual systems adapted to Martian conditions. These constitute our contributions to AI for life science and neuroscience. Besides, the principles derived from these biological studies can translate into practical engineering solutions. In practice, our findings suggest a minimalist yet effective approach to task-specific camera filter design, optimising the spectral response function to meet application-driven demands. Code repository: https://github.com/luyijie-research/PaleVision.

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