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Abstract In plant immunity, a well-orchestrated cascade is initiated by the dimerization of receptor-like kinases (RLKs), followed by the phosphorylation of receptor-like cytoplasmic kinases (RLCKs) and subsequent activation of NADPH oxidases for ROS generation. Recent findings by Zhong et al. illustrated that a maize signaling module comprising ZmWAKL-ZmWIK-ZmBLK1-ZmRBOH4 governs quantitative disease resistance to grey leaf spot, a pervasive fungal disease in maize worldwide, unveiling the conservation of this signaling quartet in plant immunity. 

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non‐ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X‐space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence‐based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. Level of Evidence5 Technical EfficacyStage 1