Machine learning models for net photosynthetic rate prediction using poplar leaf phenotype data

摘要

Background As an essential component in reducing anthropogenic CO 2 emissions to the atmosphere, tree planting is the key to keeping carbon dioxide emissions under control. In 1992, the United Nations agreed to take action at the Earth Summit to stabilize and reduce net zero global anthropogenic CO 2 emissions. Tree planting was identified as an effective method to offset CO 2 emissions. A high net photosynthetic rate (Pn) with fast-growing trees could efficiently fulfill the goal of CO 2 emission reduction. Net photosynthetic rate model can provide refernece for plant’s stability of photosynthesis productivity.