High-resolution mapping of Blueberry scorch virus incidence using RGB and multispectral UAV images and deep learning☆

Blueberry scorch caused by Blueberry scorch virus (BlScV) is a destructive disease, which can result in substantial yield decline and pose a significant threat to the viability of well-established highbush blueberry fields in North America and other regions. Early detection of the disease in the field, removal of infected bushes, and control of its spread via aphids to other fields or regions are critical for managing this disease. Visual assessment of Blueberry scorch symptoms is the predominant method for identifying and estimating the disease, which, however, is labourintensive, tedious, and inefficient. Unmanned Aerial Vehicle (UAV)-based imaging is a powerful remote sensing tool for crop monitoring with several advantages, such as flexibility to acquire images of different pixel sizes, short revisit time intervals, reduced susceptibility to cloud interference, and flexibility to equip with different sensors. This study aims to collect UAV images to detect and map BlScV-infected blueberry plants using a cutting-edge deep learning model. Images of different pixel sizes acquired by an RGB sensor, and a multispectral sensor were compared to evaluate their detection accuracies. To ensure comprehensive information dependency extraction at close-, mid-, and long-ranges, the deep learning techniques developed in this study incorporate various computer vision-based mechanisms, such as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), and Self-Attention (SA) modules. Through these innovations, the deep learning algorithm, called InceptionLSA, obtained the highest average accuracy of 76.33% and 70.00% at a 20 cm pixel size of the multispectral and RGB images, respectively.