<![CDATA[Abstract. Khaerana, Musa Y, Patandjengi B, Riadi M. 2026. Physiological and optical indicators of tungro severity across rice varieties with different resistance levels. Asian J Agric 10 (1): g100110. https://doi.org/10.13057/asianjagric/g100110. Tungro disease is a serious threat to rice production, with potential yield losses reaching 99% depending on the severity. This study evaluated the physiological response of rice plants to tungro infection, focusing on chlorophyll and anthocyanin content and light interaction characteristics. The study was conducted on six rice varieties with varying resistance levels (TN1, Inpari 13, Inpari 30, Inpari 36, Inpari 37, and M70D) using a factorial Randomized Block Design (RBD) with three replications. Tungro infection was established through controlled inoculation using two adult green leafhoppers (Nephotettix virescens) per plant and confirmed by PCR targeting Rice Tungro Bacilliform Virus (RTBV). Disease severity was assessed using a visual scale ranging from 1 (no symptoms) to 9 (severe stunting and leaf discoloration). Analysis of variance revealed a significant infection × variety interaction, indicating that physiological and optical responses to tungro differed among rice varieties according to their resistance level. The results showed that chlorophyll a content decreased by up to 42.8% in the susceptible variety (TN1), while chlorophyll b remained relatively stable (p>0.05). Anthocyanin content increased up to 2.7-fold in plants with a severity score of 9 compared to healthy plants. Tungro infestation reduced light absorption by up to 38.6% and increased reflection and transmission by 21.4% and 24.7%, respectively, indicating a response to mesophyll tissue damage. These findings suggest that a combination of physiological and spectral parameters can be used as an early indicator of tungro infection. This approach can potentially be developed as a rapid and non-destructive phenotyping method for breeding tungro-resistant rice varieties and to support precision optical sensor-based detection systems.]]>
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