Integrative hormone-metabolite tracking reveals ultra-early immune responses to anthracnose in tea cultivars

Plant fungal pathogens, particularly anthracnose, pose a significant threat to tea production by adversely affecting both yield and quality. Detecting these pathogens has been challenging due to the subtlety of their symptoms, making early identification critical for mitigating damage. The purpose of this study was to develop a novel, rapid, non-destructive, and in situ method for the ultra-early detection of tea anthracnose. The study employed hyperspectral imaging (HSI) in combination with the HybridSN-CBAM deep learning algorithm to detect tea anthracnose. A hormone–metabolite analysis was conducted to explore spectral response mechanisms, and a PLSR-based quantitative model was developed to correlate spectral data with key metabolic markers. Additionally, transmission electron microscopy (TEM) was used to examine cellular responses in resistant tea varieties. The results revealed that the HybridSN-CBAM method could detect anthracnose at least 12 h earlier than traditional methods. Metabolomics analysis identified key markers, including hormones, differential metabolites, and physiological indicators. A PLSR model demonstrated high predictive accuracy (Rp 2 > 0.8) for these metabolites. TEM imaging showed that resistant tea varieties exhibited better structural preservation and enhanced defense mechanisms. This study demonstrates the potential of using HSI and deep learning for ultra-early, non-destructive plant disease detection. The findings contribute to precision agriculture by enabling real-time monitoring of plant health and promoting sustainable agricultural practices. • HSI combined with HybridSN-CBAM deep learning enables early detection of tea anthracnose. • Hormonal and metabolomic analyses track the effectiveness of the detection method. • Spectral data correlates strongly with key metabolites (R 2 > 0.8). • TEM reveals cellular responses in resistant tea varieties. • Transferable framework for real-time physiological monitoring across multiple fields.