Wheat Cultivation Suitability Evaluation with Stripe Rust Disease: An Agricultural Group Consensus Framework Based on Artificial-Intelligence-Generated Content and Optimization-Driven Overlapping Community Detection

• Published in: Plants (Basel) Vol. 14; no. 12; p. 1794
• Main Authors: Xu, Tingyu, Cui, Haowei, Song, Yunsheng, Zhang, Chao, Alghamdi, Turki, Aborokbah, Majed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.06.2025; MDPI
• Subjects: Accuracy; Agricultural industry; Agricultural management; Agricultural production; Agricultural societies; Agriculture; Artificial intelligence; artificial intelligent generated content; China; Clustering; Crop diseases; Crop growth; Cultivation; Datasets; Decision making; Disease control; Diseases; Efficiency; Ethiopia; Evaluation; Food security; Food supply; Grain cultivation; Graph neural networks; India; Intelligence; Knowledge; Mathematical models; Multiple criterion; Neural networks; Optimization; plant disease detection; Plant structures; Precision agriculture; Propagation; Role playing; Sensitivity analysis; Social networks; Social organization; Stripe rust; Subgroups; Turkey; Wheat; wheat cultivation; wheat strip rust disease; Turkey; China; Ethiopia; India; precision agriculture; artificial intelligent generated content; wheat cultivation; plant disease detection; wheat strip rust disease

Plant modeling uses mathematical and computational methods to simulate plant structures, physiological processes, and interactions with various environments. In precision agriculture, it enables the digital monitoring and prediction of crop growth, supporting better management and efficient resource...