In order to rapidly and nondestructively detect chlorophyll content in leaves, a handheld multi-spectral imaging system was developed in this study to collect multispectral images of oilseed rape leaves. The pseudo-inverse method was introduced to reconstruct the multispectral reflectance at 6 wavebands (460, 520, 660, 740, 840 and 940 nm) to the hyperspectral reflectance at 512 wavebands in the range of 379~1 023 nm with the aim to improve the spectral resolution. The partial least square regression (PLSR) was then used to build a model to predict chlorophyll content in leaves based on the reconstructed hyperspectral reflectance. The results showed that the reflectance in the visible range of the reconstructed hyperspectral presented a high relationship with the chlorophyll content. The performance of PLSR model using reconstructed spectrum as inputs was evaluated using the parameters of the determination coefficient of prediction set ($R_p$), root mean square error of prediction (RMSEP) and residual prediction deviation (RPD) with the values of 0.78, 1.50 and 2.14, respectively, which was better than that using original spectrum at 4 wavebands (460, 520, 660 and 740 nm) with the values of $R_p^2$, RMESP and RPD of 0.72, 1.85 and 1.88, respectively. The results demonstrated that the combination multispectral imaging with spectral reconstruction technology could improve the predicting ability of the PLSR model and this technology can be used for monitoring physiology and nutrient status in oilseed rape leaves.