Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management

Abstract

Advanced sensing technologies and deep learning models are needed for automatic recognition of pathogens to protect trees in orchards. This study developed a fire blight disease detection and infected area segmentation system using image processing and deep learning approaches to automate the detection process in a complex apple orchard environment for site-specific management. Two types of images were acquired: multispectral images from an unmanned aerial vehicle (UAV) using a multispectral camera and red–greenblue (RGB) images from the ground using two different cameras. Multispectral images were preprocessed and used for image feature analysis by calculating vegetation indices, including excessive blue (ExB), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), modified ratio vegetation index (RVI), and triangular blueness index (TBI). Vegetation indices were calculated from a total of 60 multispectral images (30 heathy and 30 fire blight infected). Results showed that RVI was most sensitive to fire blight infection among the six indices. A support vector machine model was used to classify unhealthy tree canopies. A Mask Region-Convolutional Neural Network (Mask R-CNN) based deep learning model was developed from RGB infected images. A total of 880 images were used for training, and 220 images were used for validation. Another 110 images were used for testing the trained Mask R-CNN model. A precision of 92.8 % and recall of 91.2 % were obtained by detecting the infected canopies using a ResNet-101 backbone and intersection over union (IoU) threshold of 0.7. The high precision demonstrates the effectiveness of Mask R-CNN for the identification and segmentation of fire blight infection in images taken in complex orchard conditions. These results prove the potential of this non-invasive sensing method in detecting disease in commercial fruit production for site-specific infected canopies removing.