Early diagnosis method of tea anthracnose based on hyperspectral imaging technology

Tea plant (Camellia sinensis) is a significant economic crop, generating substantial economic benefits for tea-producing regions. Anthracnose is one of the most destructive fungal diseases in tea plants, making its early diagnosis and dynamic monitoring crucial for ensuring tea yield and quality. However, there is currently a lack of effective methods for early in situ diagnosis of anthracnose at the leaf scale. This study utilized two tea cultivars, “Zhongcha 108” and “Longjing 43”, as experimental materials to analyze the dynamic changes of four hormones, six defense enzymes, and three photosynthetic pigments in both anthracnose-resistant and susceptible varieties. Hyperspectral imaging technology was employed to capture leaf images at five stages of anthracnose infection (0, 12, 24, 36 and 72 hours). By analyzing the spectral response characteristics of tea leaves from the two varieties at different infection stages, principal component analysis (PCA) was employed for unsupervised clustering analysis of the hyperspectral data of tea leaves. Furthermore, the dynamic monitoring models were established for hormones, defense enzymes, and photosynthetic pigments of tea leaves. Additionally, the vertex component analysis (VCA) algorithm was used for unmixing analysis of hyperspectral image of tea leaves. For hormones, the concentrations of salicylic acid (SA) and abscisic acid (ABA) were comparable between the two cultivars and exhibited similar change trends with increasing significantly at 24 hours post-inoculation (hpi) and peaking after 72 hpi. In contrast, pronounced differences were observed in the content of jasmonic acid (JA) and indole-3-acetic Acid (IAA). The JA content in “Zhongcha 108” increased at a faster rate compared to “Longjing 43”. Furthermore, throughout the infection period (except at 0 hpi), the IAA content in “Zhongcha 108” consistently remained more than double that in “Longjing 43”. These divergent hormonal responses are likely associated with the differential resistance to anthracnose observed between the two cultivars. The activities of five defense enzymes (peroxidase (POD), superoxide dismutase (SOD), malondialdehyde (MDA), phenylalamine ammonia lyase (PAL), and polyphenol oxidase (PPO)) in both cultivars increased with the duration of infection, reaching their maximum levels after 72 hpi. Notably, the PPO activity in “Zhongcha 108” was higher than that in “Longjing 43”. Additionally, the catalase (CAT) activity in “Zhongcha 108” displayed an upward trend, whereas it declined in “Longjing 43”. These findings suggest that PPO and CAT may play significant roles in the tea plant's resistance mechanism against anthracnose. The contents of photosynthetic pigments in two cultivars, including chlorophyll a, chlorophyll b, and carotenoids, decreased progressively with the extension of infection time and reached their minimum values after 72 hpi. For hyperspectral imaging, distinct spectral features (peak and valley positions) were observed between the two tea varieties at different infection stages, reflecting the dynamic changes of component contents of tea leaves. Clustering results based on the degree of infection showed that samples at each stage could be completely separated in the principal component space (cumulative contribution rate > 96%). Partial least squares regression (PLSR) was used to establish a quantitative model between the average spectrum of tea leaves and physiological and biochemical indicators, with a maximum correlation coefficient of 0.8924. Through feature wavelength selection algorithm (competitive adaptive reweighted sampling, CARS), the number of model variables was reduced from 288 to 10, and the performance of most quantitative models was improved. The spectral unmixing method achieved in-situ visualization of the spatiotemporal dynamics of disease lesions at the pixel scale and enabled early diagnosis of anthracnose 12 hours after inoculation, which was 12～24 hours earlier than the polymerase chain reaction (PCR) method. This study provides new technical support for disease prevention and control in tea gardens, and offers a new perspective for studying the interaction mechanism between plants and fungal diseases.