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. Based on the fruit binary map and RGB-D depth image, Euclidean clustering is then applied to group the point cloud into a set of individual fruits. Next, a multiple three-dimensional (3D) line-segments detection method is developed to reconstruct the segmented branches. Finally, the 3D pose of the fruit is estimated using its center position and nearest branch information. A dataset was acquired in an outdoor orchard to evaluate the performance of the proposed method. Quantitative experiments showed that the precision and recall of guava fruit detection were 0.983 and 0.948, respectively; the 3D pose error was 23.43° ± 14.18°; and the execution time per fruit was 0.565 s. The results demonstrate that the developed method can be applied to a guava-harvesting robot.

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. The synthetic pear flower targets and the backgrounds of the original pear flower images were used as the inputs of the YOLO-PEFL model. ShuffleNetv2 embedded by the SENet (Squeeze-and-Excitation Networks) module replacing the original backbone network of the YOLOv4 model formed the backbone of the YOLO-PEFL model. The parameters of the YOLO-PEFL model were fine-tuned to change the size of the initial anchor frame. The experimental results showed that the average precision of the YOLO-PEFL model was 96.71%, the model size was reduced by about 80%, and the average detection speed was 0.027s. Compared with the YOLOv4 model and the YOLOv4-tiny model, the YOLO-PEFL model had better performance in model size, detection accuracy, and detection speed, which effectively reduced the model deployment cost and improved the model efficiency. It implied the proposed YOLO-PEFL model could accurately detect pear flowers with high efficiency in the natural environment.

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. A lightweight backbone is first deployed to extract low-level and high-level features. These features may contain redundant information in their channel and spatial dimensions, so a channel and spatial attention module is proposed to enhance informative channels and spatial locations. On this basis, a feature aggregation module is investigated to fuse the low-level details and high-level semantics to improve segmentation accuracy. A tree-part dataset with 891 RGB images is collected, and each image is manually annotated in a per-pixel fashion. Experiment results show that when using MobileNetV3-Large as the backbone, the proposed network obtained an intersection-over-union (IoU) value of 63.33 and 66.25% for the branches and fruits, respectively, and required only 2.36 billion floating point operations per second (FLOPs); when using MobileNetV3-Small as the backbone, the network achieved an IoU value of 60.62 and 61.05% for the branches and fruits, respectively, at a speed of 1.18 billion FLOPs. Such results demonstrate that the proposed network can segment the tree-parts efficiently without loss of accuracy, and thus can be applied to the harvesting robots to plan collision-free paths.

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. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.