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In the process of maize breeding, the development degree of maize ear is one of the most important parameters for yield related traits. In order to improve the degree of automation and accuracy of maize ear development degree detection, a measurement method was proposed based on machine vision technology. An identification model was constructed on the basis of random forest principal at first. The model was composed of a group of weak classifiers which were independent and identically distributed. The weak classifiers selected samples from the input training samples randomly along columns and rows. The experiment which compared random forest model with decision tree model on the classification effect showed that random forest classifier could not only avoid over-fitting and local convergence effectively but also have good generalization ability. Then, in order to determine the optimal number of weak classifiers, six random forest models were built. Their weak classifier number were separately one-eightieth, one-fortieth, one-twentieth, one-tenth, one-fifth, one-fourth of training samples count. The results showed that the model had good accuracy and stability when the number of weak classifiers was one-twentieth of training samples count. Finally, the optimal random forest model was used as the classifier to build the automatic maize ear development degree detection method. The experiment results showed that the measurement accuracy on length of each area was more than 95% and the measurement speed was more than 30 maize ears per minute.