A Track-Type Orchard Mower Automatic Line Switching Decision Model Based on Improved DeepLabV3+

To achieve unmanned line switching operations for a track-type mower in orchards, an automatic line switching decision model based on machine vision has been designed. This model optimizes the structure of the DeepLabV3+ semantic segmentation model, using semantic segmentation data from five stages of the line switching process as the basis for generating navigation paths and adjusting the posture of the track-type mower. The improved model achieved an average accuracy of 91.84% in predicting connected areas of three types of headland environments: freespace, grassland, and leaf. The control system equipped with this model underwent automatic line switching tests for the track-type mower, achieving a success rate of 94% and an average passing time of 12.58 s. The experimental results demonstrate that the improved DeepLabV3+ model exhibits good performance, providing a method for designing automatic line switching control systems for track-type mowers in orchard environments.