Abstract: Early plant disease development can involve changes in growth, physiological activity, the local microenvironment, and chemical metabolism before visible symptoms appear. Individual measurements often have limited diagnostic specificity because normal development and abiotic stresses can produce similar responses. Flexible wearable sensors establish mechanically compliant interfaces with plant organs, enabling continuous acquisition of signals relevant to these processes. Combining complementary sensing modalities offers opportunities to improve the interpretation of plant abnormalities and support timely disease management. This review examines the sensing foundations, joint analysis strategies, validation evidence, and deployment challenges of multimodal flexible wearable systems for in situ plant disease monitoring. The review first clarifies the distinctions among multiparameter sensing, multimodal sensing, and data fusion. Multiparameter sensing concerns the acquisition of multiple measured variables, whereas multimodal sensing combines different sensing approaches or information types. Data fusion describes how information is jointly processed and is not a prerequisite for a system to be considered multimodal. Advances in monitoring growth deformation, physiological and microenvironmental variables, and disease-related chemical signals are then summarized. The discussion connects the biological relevance of these signals with sensor design, transduction mechanisms, and plant interface requirements, while examining the limitations of individual indicators for distinguishing pathogen infection from other sources of stress. The review subsequently examines how sensor integration and joint analysis contribute to disease monitoring. Environmental background compensation, temporal complementarity, feature fusion, and combined decision-making are identified as major approaches to exploiting complementary information. Environmental measurements can help interpret fluctuations in physiological and chemical responses, while differences in response timing can provide additional information for discriminating similar stress conditions. Feature-level and decision-level analyses offer further opportunities to combine evidence across sensing channels. These contributions depend on the sensing configuration, target condition, and evaluation procedure; increasing the number of channels alone does not establish an improvement in disease identification. Representative systems are compared in terms of their sensing configurations, acquisition and analysis methods, validation targets, and reported performance. Evidence from pathogen inoculation experiments is distinguished from findings obtained under other biotic stresses, abiotic stresses, and growth-monitoring conditions. Studies involving viral, bacterial, and oomycete infections provide direct evidence for selected monitoring applications. Nevertheless, few investigations combine pathogen validation with quantitative comparisons between individual and combined sensing channels and extended field assessment. Reported measurement times, monitoring durations, detection times after inoculation, and lead times before visible symptoms also require separate interpretation. Existing findings suggest that complementary signals can improve the characterization of plant abnormalities, reduce environmental interference, and assist the interpretation of stress responses under specific experimental conditions. Their broader diagnostic benefits across crops, pathogens, and environments remain to be established. Key challenges include maintaining stable interfaces during plant growth, limiting disturbance to plant tissues, reducing drift and channel interference, aligning signals with different response times, sustaining power supply, and interpreting heterogeneous time-series data. Future research should connect breathable and mechanically compatible interfaces with sensor integration and calibration, low-power operation, energy harvesting, wireless communication, and reliable analytical models. Validation should include appropriate pathogen and abiotic stress controls, comparisons of individual channels and channel combinations under matched conditions, and independent assessment across plants, experimental batches, and field environments. Coordinated deployment at organ, plant, and population scales could further connect continuous sensing with agricultural monitoring networks. Progress in these areas would strengthen the contribution of multimodal flexible wearable sensors to early disease warning and precision management, with diagnostic claims supported by clearly defined biological tasks and experimental evidence.