Abstract
This paper delves into the key research directions of crop molecular breeding and intelligent technologies in future agriculture. With advancements in multi-omics data integration and analysis, researchers can more precisely identify genes associated with target traits. The deep application of artificial intelligence and machine learning has greatly enhanced breeding efficiency and decision-making accuracy. The optimized application of gene editing technology combined with big data has accelerated improvements in disease resistance, stress tolerance, and nutritional value. The establishment of global breeding big data platforms has promoted data sharing, speeding up the global crop improvement process. Automated phenotypic data collection and analysis technologies, using tools such as drones and sensors, have increased data accuracy and efficiency. Molecular breeding techniques are gradually integrating with sustainable agriculture principles, leading to the development of new varieties that are adapted to climate change and reduce environmental impact. The application of digital twin technology accelerates the development of new varieties under different environmental conditions through virtual modeling. Personalized breeding technologies meet the increasingly diverse demands of consumers through targeted breeding, driving agricultural products toward precision and high-added value.
