Discrimination of areas infected with coffee leaf rust using a vegetation index

Coffee leaf rust is for the coffee industry potentially one of the causes of a sustainability crisis. Currently, on-site disease detection is the only effective method to fell coffee trees for prevention of the infection. However, accurate infection detection over wide areas is difficult when conducted by ground surveys. Here, we examine the application of a remote sensing method. The Normalized Difference Vegetation Index (NDVI) values of coffee farms were computed using satellite images and compared with the results of the ground truth. We found that the standard deviation of the NDVI value (sigma(NDVI)) in damaged farms increases as the average NDVI value decreases. This fact implies that the disease progresses in-homogeneously inside a damaged area. In the present analysis, up to 94.1% of the damaged farms were discriminated by combining the NDVI and sigma(NDVI) thresholds when 75.0% of the damaged farms had NDVI values under 0.732 and sigma(NDVI) over 0.044. Our monitoring method enabled us to take early-stage countermeasures against the infection, and it could be applied to other vegetation diseases.