Machine learning algorithms are helping scientists analyze and predict grain quality and nutritional content for rice breeding 

The technology promises to accelerate the development of rice varieties that meet consumer preferences, enhance nutritional value, and contribute to global food security.  

By Dr. Rhowell Tiozon and Glenn Concepcion

Over 3.5 billion people, particularly in Asia and Africa, rely on rice for a significant portion of their caloric intake every day, making its quality and nutrition critical for health and well-being. However, the science of rice breeding has traditionally focused more on yield, sometimes at the expense of grain quality and nutrition. This reflects the fact that key traits like texture, flavor, and nutritional content are influenced by intricate genetic and metabolic networks, which have long posed challenges for breeders due to their complexity and the limitations of earlier technologies.

Recent research by IRRI scientists and their collaborators have employed advanced breeding technologies and machine learning to unlock insights from large-scale datasets on rice chemistry, genetics, and sensory attributes, shedding light on the intricate factors that contribute to both the nutritional content and palatability of rice. This research recognizes the key role of consumer preferences and nutritional value in rice breeding, expanding beyond traditional yield-focused approaches, and leverages the power and potential of artificial intelligence, high-throughput phenotyping, and genomic analysis to accelerate the development of rice varieties that meet diverse regional demands and combat malnutrition.