Abstract
ABSTRACTThis 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.
Highlights
In recent years, the prediction of effective challenges on the crop yield such as climate change effects, diseases, and pests has become a critical issue in food managing strategies [1]
As shown in the literature, radiometric calibration is considered as the main step in unmanned aerial vehicles (UAVs) based plant disease detection, and measurements of reference targets are implemented in empirical line based methods by default, while the efficacy and necessity of this step is unclear
This study investigated the necessity of radiometric calibration in UAV based multispectral imagery for plant tree disease detection and classification applications
Summary
The prediction of effective challenges on the crop yield such as climate change effects, diseases, and pests has become a critical issue in food managing strategies [1]. Nondestructive remote sensing data permits measurement of biophysical and biochemical parameters of plants for nondestructive monitoring of plant growth and health [3,4,5]. These methods are based on the optical properties of plants and can be used for identifying nutrient deficiencies, plants diseases, minimum water and excess water, insect damage, weeds, etc. Combination of plant knowledge and remote sensing data can provide information for plant management and even initial advices about plant stress to prevent the spread of disease or pest infestation by adopting appropriate reactions in the early stages of stress
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