Study on the fusion of improved YOLOv8 and depth camera for bunch tomato stem picking point recognition and localization - Song, Guozhu; Wang, Jian; Ma, Rongting; Shi, Yan; Wang, Yaqi (Frontiers in Plant Science, 2024)
When harvesting bunch tomatoes, accurately identifying certain fruiting stems proves challenging due to their obstruction by branches and leaves, or their similarity in colour to the branches, main vines, and lateral vines. Additionally, irregularities in the growth pattern of the fruiting pedicels further complicate precise picking point localization, thus impacting harvesting efficiency. Moreover, the fruit stalks being too short or slender poses an obstacle, rendering it impossible for the depth camera to accurately obtain depth information during depth value acquisition. To address these challenges, this paper proposes an enhanced YOLOv8 model integrated with a depth camera for string tomato fruit stalk picking point identification and localization research. Initially, the Fasternet bottleneck in YOLOv8 is replaced with the c2f bottleneck, and the MLCA attention mechanism is added after the backbone network to construct the FastMLCA-YOLOv8 model for fruit stalk recognition. Subsequently, the optimized K-means algorithm, utilizing K-means++ for clustering centre initialization and determining the optimal number of clusters via Silhouette coefficients, is employed to segment the fruit stalk region. Following this, the corrosion operation and Zhang refinement algorithm are used to denoise the segmented fruit stalk region and extract the refined skeletal line, thereby determining the coordinate position of the fruit stalk picking point in the binarized image. Finally, the issue of missing depth values of fruit stalks is addressed by the secondary extraction method to obtain the depth values and 3D coordinate information of the picking points in RGB-D camera coordinates. The experimental results demonstrate that the algorithm accurately identifies and locates the picking points of string tomatoes under complex background conditions, with the identification success rate of the picking points reaching 91.3%. Compared with the YOLOv8 model, the accuracy is improved by 2.8%, and the error of the depth value of the picking points is only ±2.5 mm. This research meets the needs of string tomato picking robots in fruit stalk target detection and provides strong support for the development of string tomato picking technology.