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

Abstract

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 the pretreatment could reduce the impact of interfering factors in complex orchard environments and enhance the quality of road extraction. Using the optimized YOLOv7 model, the precision, recall, mAP, and F1-score for roadside fruit cluster detection were 88.9%, 89.7%, 93.4%, and 89.3%, respectively, all of which were higher than those of the YOLOv5 model and were more suitable for roadside grape recognition. Compared to the identification results obtained by the grape detection algorithm alone, the proposed synchronous algorithm increased the number of fruit identifications by 23.84% and the detection speed by 14.33%. This research enhanced the perception ability of robots and provided a solid support for behavioral decision systems.

A Context-Aware Navigation Framework for Ground Robots in Horticultural Environments

“…Cheng Peng et al [ 9 ] proposed an end detection method for robots to realize autonomous navigation and obstacle avoidance in orchards without GNSS signals, which used the drastic changes in the statistical distribution of points sensed by the depth camera at the end of the robot, based on the real-time perception and response of the robot to the surrounding environment, combined with the semantic map, and further improved the navigation accuracy and safety of the robot in the orchard. In response to the needs of orchard spraying operations, Zhou, Xinzhao et al [ 10 ] constructed an environmental perception and map construction strategy based on three-dimensional LiDAR in the complex environment of the orchard, based on the three modules of “perception–decision–control” of the unmanned system, and implemented it under different working conditions. The obstacle avoidance performance and navigation accuracy of the autonomous navigation spray vehicle were verified, and the feasibility of the developed system was verified.…”

Three-dimensional reconstruction of the furrow shape in orchards using a low-cost lidar

“…With the rapid progress of industrialization and the aging of the population, the rural labor force is rapidly dwindling (Wu et al, 2021;Zhou et al, 2023b). This has led to an increasing conflict between labor demand and labor cost, which is having a significant adverse impact on traditional manual ditch fertilization methods (Akdemir et al, 2022).…”