In this study, a new method consisting contact imaging and concentrated light beam injection was used to predict the indices related to the quality of tomato fruit and also to determine the ripening stage. In total 220 tomato samples were used belonging to six stages of ripening and two stages of storage. Contact images were taken by the RGB smartphone camera. After selecting the superior features of contact images by stepwise regression method, multilayer perceptron artificial neural networks were used to create prediction and classification models. The best prediction performance was obtained using white light for a* (CIELAB color space), titratable acidity, and soluble solid content, 650 nm laser light for carotenoid, combination of 532 and 650 nm lasers for L* (CIELAB color space), elasticity, and lycopene, and combination of 650 and 780 nm wavelengths for total chlorophyll. For classification of tomatoes based on their ripening stage, the white light was also found to be the best light source. Based on the architecture and the bias and weight values of neurons of created prediction/classification models in MATLAB, an application called TomatoScan was developed for Android smartphones. The results of evaluating the TomatoScan app were almost similar to the results obtained in the test stage of neural network models using MATLAB software. Based on the results obtained for the testing dataset, the correlation coefficient (R) values for estimating L*, a*, elasticity, total chlorophyll, carotenoid, lycopene, titratable acidity, and soluble solid content were 0.901, 0.964, 0.856, 0.664, 0.824, 0.923, 0.816, and 0.792, respectively, while the corresponding values for the mean square error were 3.549, 13.485, 0.000, 14.070, 0.065, 39.198, 0.058, and 0.259, respectively. TomatoScan was also able to determine the ripening stage of tomatoes with overall accuracy of 75.00 %.
