TY - JOUR

T1 - High-resolution UAV-based blueberry scorch virus mapping utilizing a deep vision transformer algorithm

AU - Jamali, A.

AU - Lu, B.

AU - Gerbrandt, E.M.

AU - Teasdale, C.

AU - Burlakoti, R.R.

AU - Sabaratnam, S.

AU - McIntyre, J.

AU - Yang, L.

AU - Schmidt, M.

AU - McCaffrey, D.

AU - Ghamisi, P.

N1 - Export Date: 18 December 2024 CODEN: CEAGE Correspondence Address: Jamali, A.; Department of Geography, 8888 University Dr, Canada; email: alij@sfu.ca Funding details: Digital Technology Supercluster Funding details: Mitacs, IT27380 Funding details: Mitacs Funding details: Natural Sciences and Engineering Research Council of Canada, NSERC, RGPIN-2022-03679 Funding details: Natural Sciences and Engineering Research Council of Canada, NSERC Funding text 1: This research was funded by Canada's Digital Technology Supercluster, Mitacs [IT27380], i-Open Technologies Inc. Terramera Inc. and the Natural Sciences and Engineering Research Council of Canada, Discovery Grant [RGPIN-2022-03679] to Bing Lu. Thanks to the owners of farm fields for permitting us to collect data there. We also thank a group of research assistants who helped with fieldwork. Funding text 2: This research was funded by Canada\u2019s Digital Technology Supercluster, Mitacs [ IT27380 ], i-Open Technologies Inc., Terramera Inc., and the Natural Sciences and Engineering Research Council of Canada , Discovery Grant [ RGPIN-2022-03679 ] to Bing Lu. Thanks to the owners of farm fields for permitting us to collect data there. We also thank a group of research assistants who helped with fieldwork.

PY - 2025/2/28

Y1 - 2025/2/28

N2 - Blueberry scorch virus (BIScV), transmitted by aphids, causes a serious disease in highbush blueberries with a significant economic impact. Early detection and mapping of the distribution of BIScV infected plants in fields are critical to implementing effective disease management practices, such as the timely removal of infected bushes and control of aphid vectors. The conventional visual plant assessment for symptoms remains dominant in BIScV detections, though it is labor-intensive, time-consuming, and costly. In recent years, the use of remote sensing techniques has become popular for in-field assessments of crop diseases and insect pests incidence, and thus provides an effective approach for detecting and mapping BIScV infections. Convolutional Neural Networks (CNNs) are among the most widely employed algorithms in remote sensing image classification. However, CNNs have some limitations in their ability to obtain global information dependency due to the convolution's constrained receptive field in each layer. To address this challenge, the self-attention mechanism utilized in Vision Transformers (ViTs) was suggested in previous studies for achieving flexible global information dependency through facilitating communication among arbitrary pixels in images. As such, we developed a CNN-ViT-based deep learning algorithm (named “Scorch Mapper”), a pixel-based classifier, that utilizes both the functionality and capabilities of CNNs in capturing local visual characteristics and ViTs for acquiring long-range information dependency for the mapping of BIScV. We also compared the developed Scorch Mapper to several other CNN– and ViT-based algorithms, including a 2D CNN, ResNet, HybridSN, Swin Transformer, Efficient Net, CMT, InFormer, and Efficient Former. Our results demonstrated the superiority of the Scorch Mapper compared to other CNN– and ViT-based algorithms. Research findings also show that the Scorch Mapper is effective and can be applied over a wide area to support BIScV mapping and monitoring. Furthermore, the developed model opens a new window for future automatic BIScV mapping utilizing cutting-edge remote sensing algorithms and technologies. © 2024 The Author(s)

AB - Blueberry scorch virus (BIScV), transmitted by aphids, causes a serious disease in highbush blueberries with a significant economic impact. Early detection and mapping of the distribution of BIScV infected plants in fields are critical to implementing effective disease management practices, such as the timely removal of infected bushes and control of aphid vectors. The conventional visual plant assessment for symptoms remains dominant in BIScV detections, though it is labor-intensive, time-consuming, and costly. In recent years, the use of remote sensing techniques has become popular for in-field assessments of crop diseases and insect pests incidence, and thus provides an effective approach for detecting and mapping BIScV infections. Convolutional Neural Networks (CNNs) are among the most widely employed algorithms in remote sensing image classification. However, CNNs have some limitations in their ability to obtain global information dependency due to the convolution's constrained receptive field in each layer. To address this challenge, the self-attention mechanism utilized in Vision Transformers (ViTs) was suggested in previous studies for achieving flexible global information dependency through facilitating communication among arbitrary pixels in images. As such, we developed a CNN-ViT-based deep learning algorithm (named “Scorch Mapper”), a pixel-based classifier, that utilizes both the functionality and capabilities of CNNs in capturing local visual characteristics and ViTs for acquiring long-range information dependency for the mapping of BIScV. We also compared the developed Scorch Mapper to several other CNN– and ViT-based algorithms, including a 2D CNN, ResNet, HybridSN, Swin Transformer, Efficient Net, CMT, InFormer, and Efficient Former. Our results demonstrated the superiority of the Scorch Mapper compared to other CNN– and ViT-based algorithms. Research findings also show that the Scorch Mapper is effective and can be applied over a wide area to support BIScV mapping and monitoring. Furthermore, the developed model opens a new window for future automatic BIScV mapping utilizing cutting-edge remote sensing algorithms and technologies. © 2024 The Author(s)

KW - BIScV

KW - Blueberry Scorch virus mapping

KW - Deep learning

KW - Plant disease

KW - UAV

KW - Vision Transformer

KW - Convolutional neural networks

KW - Mapping

KW - Multilayer neural networks

KW - Plant diseases

KW - Unmanned aerial vehicles (UAV)

KW - Weed control

KW - Blueberry scorch virus mapping

KW - Convolutional neural network

KW - Economic impacts

KW - Global informations

KW - High resolution

KW - In-field

KW - Information dependencies

KW - Vision transformer

KW - Convolution

KW - algorithm

KW - aphid

KW - image resolution

KW - machine learning

KW - remote sensing

KW - unmanned vehicle

KW - virus

U2 - 10.1016/j.compag.2024.109726

DO - 10.1016/j.compag.2024.109726

M3 - Journal article

VL - 229

JO - Computers and Electronics in Agriculture

JF - Computers and Electronics in Agriculture

SN - 0168-1699

M1 - 109726

ER -