Engineering crop flower morphology facilitates robotization of cross-pollination and speed breeding

Abstract

Artificial intelligence (AI) and robots offer vast opportunities in shifting toward precision agriculture to enhance crop yields, reduce costs, and promote sustainable practices. However, many crop traits obstruct the application of AI-based robots. One bottleneck is flower morphology with recessed stigmas, which hinders emasculation and pollination during hybrid breeding. We developed a crop-robot co-design strategy in tomatoes by combining genome editing with artificial-intelligence-based robots (GEAIR). We generated male-sterile lines bearing flowers with exserted stigmas, and then trained a mobile robot to automatically recognize and cross-pollinate those stigmas. GEAIR enables automated F₁ hybrid breeding with efficiency comparable to manual pollination and facilitates the rapid breeding of stress-resilient and flavorful tomatoes when combined with de novo domestication under speed-breeding conditions. Multiplex gene editing in soybean recapitulated the male-sterile, exserted-stigma phenotype, potentially unlocking robotized hybrid breeding. We demonstrate the potential of GEAIR in boosting efficiency and lowering costs through automated, faster breeding of climate-resilient crops.