Biological speckle technology can non-destructively characterize the microbial activity and has gained significant attention in the field of agricultural product quality control. However, traditional speckle image processing methods do not readily distinguish various bioactive areas, and errors are generated in the quality assessment and classification process. A new image processing and analysis method based on wavelet energy spectrum is proposed to address this issue. First, the image is decomposed based on an orthogonal wavelet. Subsequently, the low-frequency components are extracted to establish an energy spectrum. Finally, the energy intensity distribution of the image is estimated, and the speckle activity is quantitatively analyzed. The results of collision experiments on the usual rock candy core apples in the market show that, compared with the traditional Fujii method, the weighted generalized difference method, the enhanced moment of inertia method, and the wavelet entropy method, the suggested approach can not only show the evolution law of apple biological activity with time; however, it also realize the distinction between the normal area and the collision area on the apple surface through the index with higher contrast and stability. The suggested approach is anticipated to be extensively employed in the field of fruit mechanical damage assessment since it is effective at identifying bruises in apple skin.