ArticleFrontiers in plant science2021
Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- A multi-stage dataset for banana bunch detection and harvesting decision support.Data in brief · 2026Article
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- A lightweight method for maize seed defects identification based on Convolutional Block Attention Module.Frontiers in plant science · 2023Article
- Remote fruit fly detection using computer vision and machine learning-based electronic trap.Frontiers in plant science · 2023Article
- Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm.Frontiers in plant science · 2023Article
- Bio-Inspired Robots and Structures toward Fostering the Modernization of Agriculture.Biomimetics (Basel, Switzerland) · 2022Review
- Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning.Sensors (Basel, Switzerland) · 2022Article
- Deep Neural Network for Point Sets Based on Local Feature Integration.Sensors (Basel, Switzerland) · 2022Article
- Design and Implementation of a UAV-Based Airborne Computing Platform for Computer Vision and Machine Learning Applications.Sensors (Basel, Switzerland) · 2022Article
- Ultra-Wideband Microwave Imaging System for Root Phenotyping.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Localization Sampling System Based on Deep Learning and Image Processing Technology.Sensors (Basel, Switzerland) · 2022Article
- Image Dehazing Using LiDAR Generated Grayscale Depth Prior.Sensors (Basel, Switzerland) · 2022Article
- Fast Location and Recognition of Green Apple Based on RGB-D Image.Frontiers in plant science · 2022Article
- Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model.Frontiers in plant science · 2022Article
- Trend Technologies for Robotic Fertilization Process in Row Crops.Frontiers in robotics and AI · 2022Article
- Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model.Frontiers in plant science · 2022Article
- Improved YOLOv4 recognition algorithm for pitaya based on coordinate attention and combinational convolution.Frontiers in plant science · 2022Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
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.
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