Advancing Crop Breeding Through Phenotyping: A Review of Methods, Applications and Low‐Cost Solutions

ABSTRACT Plant phenotyping plays an important role in modern plant breeding by linking genetic potential with agronomic performance. Traditional phenotyping methods, although successful, are often labour‐intensive, costly and time‐consuming, creating bottlenecks in breeding programmes, especially in resource‐constrained settings. High‐throughput phenotyping (HTP) technologies, incorporating advanced imaging systems, sensor networks and artificial intelligence (AI), have revolutionized trait evaluation by allowing rapid, precise and automated measurements. However, the general adoption of HTP remains limited due to high costs and technical requirements. To handle these challenges, affordable phenotyping solutions such as smartphone‐based imaging, low‐cost sensors, hardware and open‐source software have emerged as viable alternatives. These cost‐effective tools enhance data collection, allow large‐scale field evaluations and bridge the gap between genetic advancements and practical breeding applications. This review highlights the evolution of phenotyping methods, compares conventional and high‐throughput approaches and explores innovative, low‐cost solutions that can democratize crop improvement. The integration of AI, machine learning (ML) and internet of things (IoT)‐based tools in phenotyping holds immense promise to accelerate breeding cycles, improve stress resilience assessment and support the development of climate‐adaptive crop varieties. Future research should focus on improving data standardization, interoperability and field‐based phenotyping capabilities to ensure equitable access to advanced breeding technologies worldwide.