Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment

This paper focused on the necessity of radiometric calibration to distinguish diseased trees in orchards based on aerial multi-spectral images. For this purpose, two study sites were selected where multispectral images were collected using a multirotor UAV. The impact of radiometric correction on plant disease detection was assessed in two ways: 1) comparison of separability between the healthy and diseased classes using T-test and entropy distances; 2) radiometric calibration effect on the accuracy of classification. The experimental results showed the insignificant effect of radiometric calibration on separability criteria. In the second strategy, the experimental results showed that radiometric calibration had a negligible effect on the accuracy of classification. As a result, the overall accuracy and kappa values for un-calibrated and calibrated orthomosaic classifications of the citrus orchard were 96.49%, 0.941, 96.57% and 0.942, respectively, using five spectral bands as well as DVI, NDRE, NDVI and GNDVI vegetation indices using a random forest classifier. The experimental results were also similar at the other study site. Therefore, the overall accuracy and kappa values for the un-calibrated and calibrated orthomosaic classifications were 95.58%, 0.913, 95.56% and 0.913, respectively, using five spectral bands as well as NDRE, BNDVI, GNDVI, DVI, and NDVI vegetation indices.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.