Pioneering Metabolomic Studies on Diaporthe eres Species Complex from Fruit Trees in the South-Eastern Poland

Abramczyk, Barbara; Pecio, Łukasz; Kozachok, Solomiia; Kowalczyk, Mariusz; Marzec-Grządziel, Anna; Król, Ewelina; Gałązka, Anna; Oleszek, Wiesław

doi:10.3390/molecules28031175

Cited by 2 publications

(2 citation statements)

References 93 publications

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“…In recent years, in the context of the modern agricultural industrial base and the rapid development of smart agriculture, the orchard industry has created substantail economic value, and it has become more prominent in agriculture [1][2][3]. However, with their limitations in perception capacity, some orchard management machines still rely on manual evaluation of tree growth and management, which is labor intensive and time consuming [4].…”

Section: Introductionmentioning

confidence: 99%

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

Bu,

Liu,

Liu

et al. 2024

Sensors

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In order to guide orchard management robots to realize some tasks in orchard production such as autonomic navigation and precision spraying, this research proposed a deep-learning network called dynamic fusion segmentation network (DFSNet). The network contains a local feature aggregation (LFA) layer and a dynamic fusion segmentation architecture. The LFA layer uses the positional encoders for initial transforming embedding, and progressively aggregates local patterns via the multi-stage hierarchy. The fusion segmentation module (Fus-Seg) can format point tags by learning a multi-embedding space, and the generated tags can further mine the point cloud features. At the experimental stage, significant segmentation results of the DFSNet were demonstrated on the dataset of orchard fields, achieving an accuracy rate of 89.43% and an mIoU rate of 74.05%. DFSNet outperforms other semantic segmentation networks, such as PointNet, PointNet++, D-PointNet++, DGCNN, and Point-NN, with improved accuracies over them by 11.73%, 3.76%, 2.36%, and 2.74%, respectively, and improved mIoUs over the these networks by 28.19%, 9.89%, 6.33%, 9.89, and 24.69%, respectively, on the all-scale dataset (simple-scale dataset + complex-scale dataset). The proposed DFSNet can capture more information from orchard scene point clouds and provide more accurate point cloud segmentation results, which are beneficial to the management of orchards.

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Section: Introductionmentioning

confidence: 99%

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

Bu,

Liu,

Liu

et al. 2024

Sensors

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“…In recent years, in the context of the modern agricultural industrial base and the great development of specialty benefit agriculture, the fruit industry has been developing rapidly, and the advantageous industries have become more prominent [1][2][3]. At present, with the expanding area of orchard planting and the rapid development of orchard agricultural equipment autonomy and intelligence, orchard management robots, which can carry out tasks such as inspection, spraying, weeding, picking, and handling, and many other areas have been widely used [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7

Gao,

Tian,

et al. 2023

Electronics

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In order to guide the orchard management robot to realize autonomous steering in the row ends of a complex orchard environment, this paper proposes setting up steering markers in the form of fruit trees at the end of the orchard rows and realizing the rapid detection of the steering markers of the orchard management robot through the fast and accurate recognition and classification of different steering markers. First, a high-precision YOLOv7 model is used, and the depthwise separable convolution (DSC) is used instead of the 3 × 3 ordinary convolution, which improves the speed of model detection; at the same time, in order to avoid a decline in detection accuracy, the Convolutional Block Attention Module (CBAM) is added to the model, and the Focal loss function is introduced to improve the model’s attention to the imbalanced samples. Second, a binocular camera is used to quickly detect the steering markers, obtain the position information of the robot to the steering markers, and determine the starting point position of the robot’s autonomous steering based on the position information. Our experiments show that the average detection accuracy of the improved YOLOv7 model reaches 96.85%, the detection time of a single image reaches 15.47 ms, and the mean value of the localization error is 0.046 m. Comparing with the YOLOv4, YOLOv4-tiny, YOLOv5-s, and YOLOv7 models, the improved YOLOv7 model outperforms the other models in terms of combined detection time and detection accuracy. Therefore, the model proposed in this paper can quickly and accurately perform steering marker detection and steering start point localization, avoiding problems such as steering errors and untimely steering, shortening the working time and improving the working efficiency. This model also provides a reference and technical support for research on robot autonomous steering in other scenarios.

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Pioneering Metabolomic Studies on Diaporthe eres Species Complex from Fruit Trees in the South-Eastern Poland

Cited by 2 publications

References 93 publications

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7

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