Abstract: Tea plant (Camellia sinensis) is one of the most significant economic crops in modern agriculture. Anthracnose is one of the most destructive fungal diseases in tea plants. It is often required for its early diagnosis and dynamic monitoring for the tea yield and quality. However, it is still lacking on the early in situ diagnosis of anthracnose at the leaf scale at present. This study aims to realize the early diagnosis of tea anthracnose using hyperspectral imaging. Two tea cultivars, “Zhongcha 108” and “Longjing 43”, were utilized as the experimental materials. Four hormones, six defense enzymes, and three photosynthetic pigments were then determined in the anthracnose-resistant and susceptible varieties. Hyperspectral imaging was employed to capture the leaf images at five stages of the anthracnose infection (0, 12, 24, 36 and 72 h). The spectral response features of the tea leaves were obtained from the two varieties at different infection stages. Principal component analysis (PCA) was employed on the hyperspectral data of the tea leaves using unsupervised clustering. Furthermore, the dynamic monitoring models were established for the hormones, defense enzymes, and photosynthetic pigments of the tea leaves. Additionally, the vertex component analysis (VCA) algorithm was used to unmix the hyperspectral image of the tea leaves. In hormones, there were the comparable concentrations of the salicylic acid (SA) and abscisic acid (ABA) between the two cultivars. The similar increasing trends were found at 24 h post-inoculation (hpi) and peaking after 72 h. In contrast, the 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 higher rate, compared with the “Longjing 43”. Furthermore, the IAA content in “Zhongcha 108” was consistently remained more than double that in “Longjing 43” in the infection period (except at 0 hpi). These divergent hormonal responses were likely associated with the differential resistance to anthracnose 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, thereby reaching their maximum levels after 72 hpi. Notably, the PPO activity in the “Zhongcha 108” was higher than that in the “Longjing 43”. Additionally, the catalase (CAT) activity in the “Zhongcha 108” displayed an upward trend, whereas it declined in the “Longjing 43”. Therefore, the PPO and CAT also played significant roles in the tea plant's resistance to the anthracnose. The contents of the photosynthetic pigments in two cultivars, including chlorophyll a, chlorophyll b, and carotenoids, decreased progressively with the extension of the infection time, thus reaching their minimum after 72 h. In hyperspectral imaging, The spectral features (peak and valley positions) were observed between the two tea varieties at different infection stages. There was the dynamic variation in the component contents of the tea leaves. Clustering results showed that the samples with the degree of infection at each stage were fully identified 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 the tea leaves and physiological and biochemical indicators, with the maximum correlation coefficient of 0.8924. The number of model variables was reduced from 288 to 10 after feature wavelength selection (competitive adaptive reweighted sampling, CARS). The performance of most quantitative models was improved after selection. The spectral unmixing was greatly contributed to the in-situ visualization of the spatiotemporal dynamics of the disease lesions at the pixel scale, particularly for the early diagnosis of anthracnose 12 h after inoculation. There was 12~24 h earlier than the polymerase chain reaction (PCR). This finding can provide the technical support for the disease prevention and control in tea gardens. The perspective can also offer for the interaction between plants and fungal diseases.