Optimizing peanut vine-inversion operations via intelligent control and semantic segmentation

In response to the unstable inversion performance and the lack of intelligent regulation methods in current mechanized peanut harvesting processes, this paper proposes an optimization method that integrates semantic segmentation with intelligent control. First, a field data acquisition system is designed and constructed using a K230 vision module to capture peanut inversion images. A modified DeepLabV3+ semantic segmentation algorithm, enhanced by the incorporation of a Channel Transformer mechanism, is then employed to perform real‑time segmentation of these images, accurately identifying regions corresponding to peanut vines and pods. Experimental results of the segmentation module demonstrate an overall accuracy of 93.28 %, an mIoU of 76.11 %, a recall of 83.08 %, and a precision of 87.65 %, with an average processing time of 0.020327 s (approximately 49.23 FPS). Secondly, an intelligent peanut inversion control system based on fuzzy control theory is developed. In this system, the inversion rate derived from the semantic segmentation is used as a feedback signal to dynamically adjust the speeds of the conveyor belt and inversion roller in real time, thereby achieving closed‑loop control of the inversion process. Field tests show that the peanut inversion control system has an average response time of 3.18 s, consistently maintains an inversion rate above 70 %, and achieves an average inversion stability of 93.12 %. This significantly enhances both the quality and efficiency of the inversion operation. This study provides an efficient and reliable intelligent regulation solution for peanut inversion operations and holds significant implications for advancing the intelligence level of peanut harvesting machinery.