The application of Quantum-inspired ant colony algorithm in automatic segmentation of tomato image

The premise realizing intelligent picking tomato is to segment tomato from complex background accurately, the quality of segmentation tomato affects the picking accuracy of the manipulator. Based on this, the paper puts forward a new type of quantum ant colony algorithm, and the traditional quantization bionic intelligent algorithm, using quantum bit probability amplitude of the individual's current location information, increase the search space. Quantum NOT gate is used to realize mutation and depend on the different fitness of individuals with different chaotic disturbance to overcome the single rotation direction of quantum door and the defects of fixed size and avoid falling into local precocious. Compared with the traditional ant colony algorithm, the quantization of ant colony algorithm has a better diversity of population and overcomes the premature stagnation phenomenon effectively in the optimization process. We use the segmentation experiment of 100 tomato images to show this method can not only segment tomato image effectively on condition of natural light but also has been improved greatly on the segmentation speed and precision.