Research on a method of fruit tree pruning based on BP neural network

Abstract

Abstract The mainstream pruning robots do not have the ability to make decisions independently. The pruning schemes are all artificially generated by experts according to the collected images. In order to improve the intelligence of pruning robot and reduce the labor cost of pruning work, it is necessary to study the robot pruning decision algorithm corresponding to different fruit tree varieties. In this paper, taking apples in the early fruit period as an example, referring to the technical principle of traditional fruit tree pruning and aiming at two types of interference in the pruning process, the back branches and interfering branches, a pruning decision algorithm based on BP neural network was proposed. The algorithm formed the training set by artificially collecting the accurate data of the spatial characteristics of the fruit tree branches and performed calibration for pruning type, and the neural network model was trained according to the calibrated data set. The model trained in the first stage showed the situation that the competition branches cannot be identified. Based on this, an improved algorithm was proposed to improve the classification performance of the competition branches. The experimental results verified that the F1 score of the method for the back branches was 0913; the F1 score for the centripetal branches was 0.867; the improved algorithm has an F1score of 0.755 for the competition branches; the overall conformed to the expectation, which could provide algorithm support for the pruning robot to make artificial intelligence decision.