Characterization of rice blast disease using greenness index, canopy temperature and vegetation indices

Blast diseases cause economically important damage to rice. Protective treatments help producers to secure good quality crops. In contrast, curative treatments based on visually detectable symptoms are often riskier and less effective because diseased crop plants may develop disease symptoms too late for curative treatments. On the other hand, the effect of blast severity levels on crop physiology (greenness index and canopy temperature) and vegetation indices may help in early detection of rice blast. Keeping this view, a field experiment was conducted at ICAR-VPKAS, Almora to study the effect of different rice blast severity levels on canopy temperature, greenness index and hyperspectral vegetation indices with 10 rice genotype each for upland and irrigated condition. The extent of disease severity was rated 0–9 based on the extent of host organ covered by symptom or lesion. It was observed that canopy temperature and greenness index was significantly influenced by blast disease severity levels for both conditions. 8 different vegetation indices having higher correlation coefficient (>0.8) was calculated. The linear regression models were developed between these indices and disease score. Out of those, MTVI based model performed best for blast disease severity assessment having R² and RPD value more than 0.85 and 2.58 respectively. So MTVI based model can be used for detecting rice blast.

• MTVI based model performed best for blast disease severity assessment.

• Canopy temperature positively correlates with blast severity levels and greenness index negatively correlates with blast severity levels.