However, the frequent occurrence of wheat diseases has become a significant issue affecting wheat production [1]. In particular, the spread of wheat yellow dwarf disease and wheat rust poses a severe threat, as these diseases are highly contagious and can cause significant damage to wheat leaves and roots, weakening photosynthesis and disrupting growth and development [2]. As a result, wheat yield and quality are significantly reduced. To address this challenge, developing a cost-effective and timely method for the large-scale, coarse-grained identification of wheat disease-affected areas is of paramount importance for enhancing disease control efficiency, safeguarding wheat yields, and ensuring national food security. Conventional plant disease detection predominantly depends on visual assessments conducted by farmers or experts, which are inherently subjective, highly reliant on individual experience, time-consuming, labor-intensive, and economically inefficient. With the advancement of deep learning, an increasing number of deep learning approaches have been applied to computer vision [3; 4; 5; 6; 7], thus advancing the development of smart agriculture [8]. [PERSON] et al. introduced an enhanced deep convolutional model for wheat disease classification, achieving an accuracy of 97.88% across 10 disease classes [9]. [PERSON] et al. developed a CNN-based model for the automatic detection and classification of apple tree disorders, achieving 97.3% accuracy [10]. Furthermore, the development of object detection techniques in deep learning has further propelled research into fine-grained plant disease detection [11]. For instance, [PERSON] et al. proposed MEAN-SSD, a lightweight CNN model for real-time apple leaf disease detection on mobile devices, achieving 83.12% mAP and 12.53 FPS [12]. [PERSON] et al. improved the SE-YOLOv5 model for tomato virus disease recognition, achieving an accuracy of 91.07% and a mean average precision of 94.10%, outperforming other models [13]. However, existing studies predominantly rely on RGB single-source images captured by ground-based handheld devices. While these methods perform well in fine-grained detection at the leaf or lesion level, they are often unsuitable for large-scale field scenarios, particularly when it comes to detecting disease-affected areas. The development of UAV-based low-altitude remote sensing technology has provided significant opportunities for precision agriculture [14; 15]. Due to its advantages, including minimal meteorological and landing site constraints, wide application range, low operational costs, and ability to capture high-resolution imagery, UAV remote sensing has been widely applied in crop yield estimation [16; 17; 18], disease diagnosis [19; 20; 21], and crop growth monitoring [22; 23; 24]. When crops are affected by diseases, their spectral responses at specific wavelengths exhibit marked alterations. By effectively capturing these spectral variations, accurate disease diagnosis can be achieved. For example, the red band can be used to detect potato late blight [25], the red-edge band is suitable for detecting oil palm orange spot disease [26], and infrared bands have also been widely applied in crop disease detection [27]. The above studies mainly focus on using a single band or detecting a single disease, neglecting the comprehensive use of multi-band information. UAVs equipped with multispectral cameras can capture images across various bands when crop diseases occur, enabling the extraction of spectral features for different diseases at different bands. Recently, an increasing number of studies have employed multispectral remote sensing technology for plant disease detection [28; 29]. For instance, [PERSON] et al. applied multi-source image fusion using RGB and multispectral UAV images for apple disease and pest classification, achieving a subset accuracy of 92.92% [30]. [PERSON] et al.