Recognition of green apples in an orchard environment by combining the GrabCut model and Ncut algorithm

Sun, Sashuang; Jiang, Mei; He, Dongjian; Lu, Yan; Song, Huaibo

doi:10.1016/j.biosystemseng.2019.09.006

Cited by 42 publications

(16 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method had a better segmentation effect for a single fruit, but it cannot get a complete fruit area for multiple fruits, and it was greatly affected by the interference of the external environment, especially the occlusion of leaves and branches. Sun, Jiang, He, Long, and Song (2019) used the three‐point circle fitting method to reconstruct the apple target. But for the key parameters in the process of fruit region extraction and segmentation, manual adjustment was still required.…”

Section: Introductionmentioning

confidence: 99%

Apple target recognition method in complex environment based on improved YOLOv4

Gao

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

In order to solve the problem of accurate recognition of apples in complex environments, this article proposes an apple recognition method based on improved YOLOv4, which can accurately locate and recognize apples in a variety of complex environments. This method uses the lightweight EfficientNet‐B0 network as the feature extraction network for apple recognition and then combines with the PANet (Path Aggregation Network) network to fuse the features in the adjacent feature layers, which improves the recognition accuracy of fruit targets. In the experiment, the apples under bagging, nonbagging and night environment are identified. The results of the experiment show that the average precision of the improved YOLOv4 method is 93.42%, the recall rate is 87.64%, and the F1 value is 0.9035, which is about 2% lower than that of the original YOLOv4. However, the storage memory required for the model trained on the improved YOLOv4 is decreased by 87.8% compared with the original YOLOv4, which is only 29.8 MB, and the recognition speed is increased by 43%, reaching 63.20 frames per second. The recognition accuracy and speed are much higher than that of the two‐stage Faster‐RCNN. The recognition accuracy is equivalent to that of Mobilenetv3‐YOLOv4, but the model of our method is smaller and the recognition speed is faster. Practical Applications Achieving rapid and accurate recognition of apples in complex environments is one of the key technologies for automatic fruit picking by picking robots, and it is also an important method and means for realizing orchard yield estimation. In order to recognize apples in the orchard environment more quickly and effectively, a light‐weight YOLOv4 target recognition method based on deep learning is proposed. The method proposed in this article can significantly reduce the storage space and computing power requirements of the device. On the basis of maintaining high accuracy, it can achieve real‐time and accurate recognition of apples in complex environments, and has great application value and practical significance for picking robot.

show abstract

Section: Introductionmentioning

confidence: 99%

Apple target recognition method in complex environment based on improved YOLOv4

Gao

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Accurate location and fast recognition of green target fruit become a new challenge. With the joint efforts of many scholars, certain progress has been made ( Lv et al, 2019a ; Sun et al, 2019 ; Behera et al, 2020 ; Ji et al, 2020 ). It is difficult to recognize green apples only from the perspective of color, and it needs to be processed or try other features.…”

Section: Introductionmentioning

confidence: 99%

Fast Location and Recognition of Green Apple Based on RGB-D Image

Sun

Luo

et al. 2022

Front. Plant Sci.

View full text Add to dashboard Cite

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.

show abstract

“…In recent years, machine learning has been widely applied to several agricultural studies, and some results have been achieved [3]. Researchers have applied computer vision techniques in apple quality inspection [4][5][6], apple pesticide residues [7], apple size assessment [8,9], and apple detection [10]. Nahina Islam et al explored the potential of machine learning algorithms for weed and crop classification in UAV images.…”

Section: Introductionmentioning

confidence: 99%

Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

Zhang

et al. 2021

Information

View full text Add to dashboard Cite

Apple flower detection is an important project in the apple planting stage. This paper proposes an optimized detection network model based on a generative module and pruning inference. Due to the problems of instability, non-convergence, and overfitting of convolutional neural networks in the case of insufficient samples, this paper uses a generative module and various image pre-processing methods including Cutout, CutMix, Mixup, SnapMix, and Mosaic algorithms for data augmentation. In order to solve the problem of slowing down the training and inference due to the increasing complexity of detection networks, the pruning inference proposed in this paper can automatically deactivate part of the network structure according to the different conditions, reduce the network parameters and operations, and significantly improve the network speed. The proposed model can achieve 90.01%, 98.79%, and 97.43% in precision, recall, and mAP, respectively, in detecting the apple flowers, and the inference speed can reach 29 FPS. On the YOLO-v5 model with slightly lower performance, the inference speed can reach 71 FPS by the pruning inference. These experimental results demonstrate that the model proposed in this paper can meet the needs of agricultural production.

show abstract

Recognition of green apples in an orchard environment by combining the GrabCut model and Ncut algorithm

Cited by 42 publications

References 12 publications

Apple target recognition method in complex environment based on improved YOLOv4

Apple target recognition method in complex environment based on improved YOLOv4

Fast Location and Recognition of Green Apple Based on RGB-D Image

Using Generative Module and Pruning Inference for the Fast and Accurate Detection of Apple Flower in Natural Environments

Contact Info

Product

Resources

About