Real-time guava tree-part segmentation using fully convolutional network with channel and spatial attention.

It is imminent to develop intelligent harvesting robots to alleviate the burden of rising costs of manual picking. A key problem in robotic harvesting is how to recognize tree parts efficiently without losing accuracy, thus helping the robots plan collision-free paths. This study introduces a real-time tree-part segmentation network by improving fully convolutional network with channel and spatial attention.

Study on Pear Flowers Detection Performance of YOLO-PEFL Model Trained With Synthetic Target Images.

Accurate detection of pear flowers is an important measure for pear orchard yield estimation, which plays a vital role in improving pear yield and predicting pear price trends. This study proposed an improved YOLOv4 model called YOLO-PEFL model for accurate pear flower detection in the natural environment. Pear flower targets were artificially synthesized with pear flower's surface features.

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review.

As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links.

Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field.

Fruit detection in real outdoor conditions is necessary for automatic guava harvesting, and the branch-dependent pose of fruits is also crucial to guide a robot to approach and detach the target fruit without colliding with its mother branch. To conduct automatic, collision-free picking, this study investigates a fruit detection and pose estimation method by using a low-cost red⁻green⁻blue⁻depth (RGB-D) sensor. A state-of-the-art fully convolutional network is first deployed to segment the RGB image to output a fruit and branch binary map.