A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Chen, Tianci; Zhang, Rihong; Zhu, Liehuang; Zhang, Shiang; Li, Xiaomin

doi:10.3390/machines9030066

Cited by 18 publications

(13 citation statements)

References 36 publications

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“…As the weight and size of the banana bunch is large with its stalk size, working in outdoor with light weight and high-speed components in a mobile robot is more preferable for complex environment. Considering this light weight sandglass residual feature extraction network has been proposed 173 by combining the structures of residual and non-residual networks with depth separable convolution to reduce the number of network parameters. The network provides a F1 score of 99.32% and Recall rate of 99.08%.…”

Section: Fruit Peduncle Detectionmentioning

confidence: 99%

“…Examples of different fruit harvesting robots. ; (a) Dual arm Tomato Harvesting Robot with SCARA like Manipulator 47 , (b) Sweet Pepper Harvesting Robot 47 Harvey platform, (c) Sweet pepper harvesting Robot 45 , (d) Thorvald II - Single rail based cartesian type multiarm system 172 , (e) Thorvald II - Strawberry harvesting with cabel driven gripper 173 , (f) Kiwifruit Harvesting Robot 12 , (g) Apple Harvesting Robot with UR5 Manipulator 11 , (h) Apple Harvesting Robot 174 , (i) Humanoid Apple 175 and Grape 55 harvesting robot .…”

Section: Fruit Harvesting Robotsmentioning

confidence: 99%

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Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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Section: Fruit Peduncle Detectionmentioning

confidence: 99%

Section: Fruit Harvesting Robotsmentioning

confidence: 99%

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Kalampokas et al (2021) applied a regression convolutional neural network (RegCNN) for executing a stem segmentation task and determined the cutting point on the stem based on a geometric model. Chen et al (2021) proposed a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network. The methods in both (Kalampokas et al, 2021) and (Chen et al, 2021) can only segment the stem of a single cluster of grape or banana.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al (2021) proposed a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network. The methods in both (Kalampokas et al, 2021) and (Chen et al, 2021) can only segment the stem of a single cluster of grape or banana. Wan et al (2022) proposed a realtime branch detection and reconstruction method applied to fruit harvesting.…”

Section: Introductionmentioning

confidence: 99%

Grasping and cutting points detection method for the harvesting of dome-type planted pumpkin using transformer network-based instance segmentation architecture

et al. 2023

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An accurate and robust keypoint detection method is vital for autonomous harvesting systems. This paper proposed a dome-type planted pumpkin autonomous harvesting framework with keypoint (grasping and cutting points) detection method using instance segmentation architecture. To address the overlapping problem in agricultural environment and improve the segmenting precision, we proposed a pumpkin fruit and stem instance segmentation architecture by fusing transformer and point rendering. A transformer network is utilized as the architecture backbone to achieve a higher segmentation precision and point rendering is applied so that finer masks can be acquired especially at the boundary of overlapping areas. In addition, our keypoint detection algorithm can model the relationships among the fruit and stem instances as well as estimate grasping and cutting keypoints. To validate the effectiveness of our method, we created a pumpkin image dataset with manually annotated labels. Based on the dataset, we have carried out plenty of experiments on instance segmentation and keypoint detection. Pumpkin fruit and stem instance segmentation results show that the proposed method reaches the mask mAP of 70.8% and box mAP of 72.0%, which brings 4.9% and 2.5% gains over the state-of-the-art instance segmentation methods such as Cascade Mask R-CNN. Ablation study proves the effectiveness of each improved module in the instance segmentation architecture. Keypoint estimation results indicate that our method has a promising application prospect in fruit picking tasks.

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“…The classification network VGGNet was applied to the detection of red dates [16] and kiwifruits [17], which improved detection performance by increasing depth; Resnet has a deeper network but lower parameters than VGGNet. The improved Resnet was discussed for apples [18], strawberries [19], waxberries [20], and banana stalks [21]. The segmentation network solves the problem of image segmentation at the pixel level, e.g., FCN and SegNet neural networks were compared in the detection of grapevine cordon shape [22], and the Deeplabv3 series was used for multiple lychee fruit-bearing branches [23] and banana stalk segmentation [24].…”

Section: Introductionmentioning

confidence: 99%

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Yang

et al. 2022

Agronomy

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The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard.

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A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Cited by 18 publications

References 36 publications

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Grasping and cutting points detection method for the harvesting of dome-type planted pumpkin using transformer network-based instance segmentation architecture

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

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