Fruits and vegetables are essential sources of vitamins that are very important for health. Tomato is one of the vegetables that has achieved great popularity over the last century. This important economic and nutritional crop belongs to the solanaceae family. In recent years, considerable progress has been carried out in developing non-destructive methods for estimation of internal qualitative parameters of agricultural products. Laser technology is one of the methods that have been used in this field. Lasers are employed over a wide range of applications in scientific researches including biomedicine, agricultural sciences, industrial materials processing, microelectronics, and avionics. Laser light backscattering imaging (LLBI) is an advanced technology applicable as a non-invasive technique for determining quality of fruits, vegetables, and processed foodstuffs. This technique is an inexpensive method for rapidly receiving relevant information for site-specific measures. The present study investigates the possibility of this method for predicting firmness, soluble solids content, titratable acidity and moisture content during tomato ripening. Fruit SSC and titratable acidity determines the taste of tomato and the ratio of them is defined as the taste index. Firmness is an important parameter for determining fruit maturity, quality grade as well as harvest time. Images of crops were obtained using a backscattering imaging set-up with lasers emitting at different wavelength bands including visible lasers of 532 and 632 nm and near infrared (NIR) lasers of 808, 980 and 1064 nm. The lasers 532, 808, 980 and 1064 nm were applied at two output power levels. Immediately after image acquisition of tomato samples, firmness, SSC, titratable acidity and moisture content were measured by standard destructive methods. Different image processing algorithms were adopted to enhance the quality of the acquired images and extract the desired parameters. Iterative Arithmetic (IA) and OTSU threshold algorithms were used in order to adjust global threshold. After background segmentation, all nonzero gray values obtained from the images were higher than or equal to threshold. The dissimilar shape of samples were then improved using lambert's cosine law and the stochastic noise were removed by medium filtering algorithms. Numerical histogram (gray scale intensities) and statistical variables of images were used separately for developing artial least squares regression (PLSR) models. The statistical variables were included total number of pixels, average, average deviation, variance, skewness and kurtosis of the back scattered images. The results showed that the total number of pixels of backscattered region were negatively correlated with firmness and positively correlated with SSC during fruit ripening. The results also indicated highest performance of firmness predictive models based on the gray scale intensities and IA threshold algorithm with correlation coefficient of R = 0.84 and root mean square error of cross validation RMSECV = 2.40 N. For estimating soluble solid content, gray scale intensities and OTSU threshold algorithm resulted to the highest performance with R=0.80, and RMSECV = 0.4°Brix. Predictions obtained for SSC were slightly lower than firmness. Finally, the models based on backscattering images did not resulted to the promising accuracies for moisture content and titratable acidity of tomatoes. Keywrods Pixel, Laser, Near infrared, Backscattering imaging, Quality, Non-destructive, Prtial least squares, Threshold