Over 380 million adults worldwide are currently living with diabetes and the number has been projected to reach 590 million by 2035. Uncontrolled diabetes often lead to complications, disability, and early death. In the management of diabetes, dietary intervention to control carbohydrate intake is essential to help manage daily blood glucose level within a recommended range. The intervention traditionally relies on a self-report to estimate carbohydrate intake through a paper based diary. The traditional approach is known to be inaccurate, inconvenient, and resource intensive. Additionally, patients often require a long term of learning or training to achieve a certain level of accuracy and reliability. To address these issues, we propose a design of a smartphone application that automatically estimates carbohydrate intake from food images. The application uses imaging processing techniques to classify food type, estimate food volume, and accordingly calculate the amount of carbohydrates. To examine the proof of concept, a small fruit database was created to train a classification algorithm implemented in the application. Consequently, a set of fruit photos (n=6) from a real smartphone were applied to evaluate the accuracy of the carbohydrate estimation. This study demonstrates the potential to use smartphones to improve dietary intervention, although further studies are needed to improve the accuracy, and extend the capability of the smartphone application to analyse broader food contents.