Abstract
Phenotyping systems propels the growth of modern agriculture, driving innovations in plant breeding, crop management, precise application of resources and smart agriculture. This review provides a comprehensive analysis of phenotyping systems, exploring their status, technological advancements, challenges and future directions. The evolution from traditional phenotyping to high-throughput phenotyping (HTP) systems with involvement of advanced imaging (visible, infrared, hyperspectral, and thermal), sensors (LIDAR and NIR), data analytics, drones and automated platforms have enabled rapid non-invasive collection of phenotypic information, significantly hastening breeding programs and improving stress tolerance studies. The integration of big data, artificial intelligence (AI) and machine learning (ML) has enhanced data management and interpretation, enabling the development of predictive models and real-time decision-making tools. Despite these advancements, several challenges persist. The technical issues such as data accuracy, resolution and consistency alongside economic concerns related to high cost of implementation, limits the widespread adoption of advanced phenotyping technologies, especially among smallholder farmers. Furthermore, the integration of these technologies with traditional farming practices and the handling of large datasets raises concerns about data privacy, ownership and interpretation. The impending growth of phenotyping lies in advancements such as the integration of AI and genomics, enabling more precise breeding through the linking of genetic information with phenotypic traits. Additionally, the development of low-cost systems is essential to democratize access to precision agriculture, particularly in developing regions. As phenotyping systems continue to advance, they will play a critical role in promoting sustainable agriculture, enhancing resource efficiency, ensuring food security and addressing global climate change.