Abstract
Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot.
Highlights
Recent years have seen an unprecedented rise in the cost of human labor, with the increase reaching up to 12–15% in 2019 (Fu et al, 2020)
A comparison was made between the modified YOLOv3 model, YOLOv4 model, and Faster R-CNN
YOLOv3 had a higher precision for detecting the inflorescence axis, which was comparable to that of the faster R-CNN
Summary
Recent years have seen an unprecedented rise in the cost of human labor, with the increase reaching up to 12–15% in 2019 (Fu et al, 2020). Buds, and inflorescence axes are three types of targets with different features. The detection of these targets is influenced by the random shading of leaves or plants, background noises, and lighting. Multi-Target Classification of Banana Fruits, Flower Buds, and Inflorescence Axes. The separate detection of each feature of the banana plants (inflorescence axes and buds) was performed using multiscale feature fusion maps This method could enhance the detection performance for targets of varying sizes and the shaded ones. The YOLOv3 still needs to be optimized when applied to the multi-target detection of bananas in a complex field environment and changing lighting conditions.
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