Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Abstract

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture. Recent advancements in high-throughput phenotyping technologies and artificial intelligence (AI) have revolutionized the field, enabling rapid and accurate assessment of crop traits on a large scale. The integration of AI and machine learning algorithms with high-throughput phenotyping data has unlocked new opportunities for crop improvement. AI algorithms can analyze and interpret large datasets, extracting meaningful patterns and correlations between phenotypic traits and genetic factors. These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection, reducing the time and cost required for variety development. However, further research and collaborations are needed to overcome the challenges and fully unlock the power of high-throughput phenotyping and AI in crop improvement. By leveraging AI algorithms, researchers can efficiently analyze phenotypic data, uncover complex patterns, and establish predictive models that enable precise trait selection and crop breeding. The aim of this review is to explore the transformative potential of integrating high-throughput phenotyping and AI in crop improvement. The review will encompass an in-depth analysis of recent advancements and applications, highlighting the numerous benefits and challenges associated with high-throughput phenotyping and intelligence.