Handheld SPAD meter is often used to measure chlorophyll content of plant and nitrogen level for some species. For plant production automation, however, it loses its popularity due to its point-by-point checking. The authors need to monitor the growing conditions of plant remotely, instantly and nondestructively. In the test, we examined optical fiber reflection spectroscopy used to measure chlorophyll content of some plant leaves, or for their SPAD prediction. The authors picked 120 leaves randomly from our campus ground or trees, among which 70 samples were chosen as calibration set and others as verification set. Each sample was water-cleaned and air-dried. To locate each measuring point precisely when using SPAD meter and spectrometer, the authors drew a circle with a diameter of 10 mm on each leave to be measured. By comparing the spectral curves of various leaves, the authors found that the spectral band between 650-750 nm was significant for SPAD modeling since this range of spectral data of leaves with the same SPAD reading was close to each other. It was showed that leave color was an unnecessary factor for SPAD prediction by reflection spectroscopy. Besides, the authors discovered that LED’s narrow spectral range used by SPAD meter should be concerned because optical fiber spectrometer has much more wide spectral range. Based on this awareness, the authors designed an adjustment factor of light to linearly rebuild spectrometer’s reflective intensity so that it reached zero outside the band 650-750 nm. Moreover, leave thickness was another influential factor for SPAD prediction since the light of SPAD meter goes through the leave while the reflective spectrometer does not. First, an equation for SPAD prediction was built with uncertain parameters. Then, a standard genetic algorithm was designed with Visual Basic 6.0 for parameter optimization. As a result, the optimal reflection band was narrowed within 683.24-733.91 nm. The result showed that leave thickness strongly affects the precision of SPAD prediction. Through the modification of leave thickness, the regression coefficient (R2) of calibration set and verification set reached 0.865 8 and 0.916 1 respectively. The test showed that optical fiber reflection spectroscopy is useful for SPAD prediction and can be used to develop remote SPAD sensor.