Automatic recognition of juicy peaches on trees based on 3D contour features and colour data

Abstract

In recent years, fruit harvesting robots have become a research hotspot for agricultural machinery. To be effective, robots should quickly and accurately detect fruits on trees in the complex environments. In this study, a method using an RGB-D (Red, green, blue, and depth space) camera combining colour data and 3D contour features for the detection of juicy peach was developed based on clustering and model segmentation. The point cloud of fruits was acquired in realistic field conditions. The pre-processed point cloud was divided into clusters by the conditional Euclidean clustering algorithm, and the 3D contour features were then used to extract fruits. The random sample consensus (RANSAC) spherical segmentation method was applied to clusters considered to contain multiple fruits that have close distance with one another. The fruit point cloud obtained is incomplete due to the limited range of camera view and actual occlusions in orchard, these clusters are considered to contain only one fruit. The progressive sample consensus (PROSAC) circular segmentation method was exploited to improve accuracy. Experiments were conducted under three situations with different occlusions. The total recognition accuracy reached 88.68%, and the average recognition processing time for a single fruit was about 292.4 ms, it can meet the requirements for fruit detection considering that the harvesting robot mainly takes the ripe peaches as object.