Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm

作者：Xinzhao Zhou, Xiangjun Zou, Wei Tang, Zhiwei Yan, Hewei Meng, Xiwen Luo · 发表于：Frontiers in Plant Science · 年份：2023 · DOI：10.3389/fpls.2023.1103276 · 被引用次数：11 · 研究领域：Smart Agriculture and AI、Advanced Chemical Sensor Technologies、Date Palm Research Studies

Accurate road extraction and recognition of roadside fruit in complex orchard environments are essential prerequisites for robotic fruit picking and walking behavioral decisions. In this study, a novel algorithm was proposed for unstructured road extraction and roadside fruit synchronous recognition, with wine grapes and nonstructural orchards as research objects. Initially, a preprocessing method tailored to field orchards was proposed to reduce the interference of adverse factors in the operating environment. The preprocessing method contained 4 parts: interception of regions of interest, bilateral filter, logarithmic space transformation and image enhancement based on the MSRCR algorithm. Subsequently, the analysis of the enhanced image enabled the optimization of the gray factor, and a road region extraction method based on dual-space fusion was proposed by color channel enhancement and gray factor optimization. Furthermore, the YOLO model suitable for grape cluster recognition in the wild environment was selected, and its parameters were optimized to enhance the recognition performance of the model for randomly distributed grapes. Finally, a fusion recognition framework was innovatively established, wherein the road extraction result was taken as input, and the optimized parameter YOLO model was utilized to identify roadside fruits, thus realizing synchronous road extraction and roadside fruit detection. Experimental results demonstrated that the proposed method based on...