AI Model Revolutionizes Crop Disease Resistance Selection

Scientists at the Chinese Agricultural Academy have developed an AI model that predicts crop resistance to diseases such as rice blast, wheat rust, and stripe rust with over 90% accuracy. This innovation utilizes machine learning to analyze genetic markers, significantly streamlining the process of selecting disease-resistant varieties. Traditionally, such selection relied on time-consuming and costly field tests, which could cost up to 10 million RMB for just 10,000 potential varieties.

The AI model, developed by researcher Kang Houxiang and his team, employs machine learning algorithms to predict disease resistance based on genomic data. This method not only reduces costs but also accelerates the breeding process, potentially revolutionizing how farmers and breeders select seeds.

Kang's journey into AI began during the COVID-19 lockdown when he taught himself Python to explore machine learning applications in agriculture. His efforts culminated in a model that accurately predicts disease resistance in new crop varieties, a breakthrough that could enhance global food security by ensuring more resilient crops.

This development underscores the transformative potential of AI in agriculture, offering a glimpse into a future where technology plays a pivotal role in enhancing crop resilience and food production efficiency.