Wheat Soil-Borne Mosaic Virus Disease Detection: A Perspective of Agricultural Decision-Making via Spectral Clustering and Multi-Indicator Feedback

• Published in: Plants (Basel) Vol. 14; no. 15; p. 2260
• Main Authors: Hou, Xue, Zhang, Chao, Song, Yunsheng, Alghamdi, Turki, Aborokbah, Majed, Zhang, Hui, La, Haoyue, Wang, Yizhen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.07.2025; MDPI
• Subjects: agricultural artificial intelligence; Agricultural production; Agriculture; Artificial intelligence; Artificial neural networks; Bayesian analysis; Clustering; Crop production; Data augmentation; Decision making; Disease detection; Disease transmission; Environmental conditions; Feedback; Group theory; Heterogeneity; Indicators; Mathematical models; Plant diseases; Plant viruses; plants disease assessment; Propagation; Sensitivity analysis; Signs and symptoms; social network; Spatial heterogeneity; Viruses; Wheat; social network; plants disease assessment; agricultural artificial intelligence
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants14152260

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts.