Experiment on automatic shape identification of hatching eggs based on improved genetic algorithm neural network

Shape inspection of hatching eggs is an important and hard work in farms, manual inspection lacks the objectivity and is time-consuming. In order to solve problems mentioned above, an automatic shape identification method was proposed based on machine vision, moment technique and improved genentic algorithm-neural network (GA-NN) algorithm. Egg shape index and radius differences were extracted as eggs shape feature parameters. An improved immune genentic algorithm was put forward to optimize topology structure of levenberg-marquardt back progagation-neural network (LMBP-NN). After egg shape index was identified , radius differences were used as inputs of LMBP-NN and its outputs were used to determine the hatching egg shape normal or not. The results indicated that the classification accuracy of this method reached 97.1% for longer eggs, 95.59% for shorter eggs, 94.87% for abnormal eggs and 95.75% for normal eggs’ respectively. It is significant for shape identification of hatching eggs automatically, which can improve detection accuracy and efficiency. The neural network system for shape identification of hatching eggs has high accuracy and generalization ability, and the algorithm is feasible and robust.