Visual observation and image analysis method of blight disease severity for resistance assessment of two rice varieties

Abstract

Bacterial Leaf Blight (BLB), caused by Xanthomonas oryzae pv. oryzae, is a major threat to global rice production, causing yield losses of up to 80%. Accurate assessment of disease severity is essential for developing resistant rice varieties and implementing effective management strategies. However, traditional visual observation methods, while widely used, are prone to subjectivity and reduced accuracy. This study evaluates the accuracy of image analysis for assessing rice plant resistance to BLB. Disease severity was assessed using both visual observation and image analysis, with results quantified through the Area Under the Disease Progress Curve (AUDPC) and infection rate calculations. Image analysis outperformed visual observation, achieving an accuracy rate above 96%, compared to less than 90% for the latter. The Ciherang variety demonstrated greater resistance to BLB, with lower AUDPC and infection rates when assessed using image analysis. Conversely, visual observation produced contradictory results, highlighting its limitations. This study concludes that image analysis provides a more objective, reproducible, and accurate approach to assessing disease severity, with implications for breeding programs and integrated disease management systems. Further research is recommended to validate these methods across a broader range of rice genotypes and environmental conditions.