Tomatoes are commercial commodities that play a major role in Iranian economy. They are considered one of the major vegetable crops in Iran because of its nutritional, consumption, processing and export value. They may be harvested at different maturity stages and each maturity stage has its characteristics of quality. On the other hand, acceptance of tomato for eating depends on many factors such as external color and stiffness. The traditional six ripeness stages for fresh-market tomatoes are based almost entirely on the external color change of the fruit from green to red (i.e. destruction of chlorophyll and synthesis of lycopene). Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. In this present experimental study aimed to develop a color image analysis procedure for identifying the ripening stages of tomato. RGB (Red, Green, and Blue) images of each tomato were captured and converted to L*a*b* values. The RGB values of the tomato were processed by the MATLAB and used to identify the stage of tomato ripeness. In addition, the mechanical, physical and chemical properties of tomato was experimentally determined. An artificial neural network (ANN) was developed with two stages of ltr" Key words: Artificial Neural Network, L*a*b*, MATLAB, Maturity, RGB, Tomato
