scholarly journals An Approach for Rice Bacterial Leaf Streak Disease Segmentation and Disease Severity Estimation

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 420
Author(s):  
Shuo Chen ◽  
Kefei Zhang ◽  
Yindi Zhao ◽  
Yaqin Sun ◽  
Wei Ban ◽  
...  
Keyword(s):  
Disease Severity ◽  
Preliminary Investigation ◽  
Semantic Segmentation ◽  
Bacterial Leaf Streak ◽  
Multi Scale ◽  
Severity Estimation ◽  
Leaf Lesion ◽  
Automatic Estimation ◽  
Rice Leaves ◽  
Serious Disease

Rice bacterial leaf streak (BLS) is a serious disease in rice leaves and can seriously affect the quality and quantity of rice growth. Automatic estimation of disease severity is a crucial requirement in agricultural production. To address this, a new method (termed BLSNet) was proposed for rice and BLS leaf lesion recognition and segmentation based on a UNet network in semantic segmentation. An attention mechanism and multi-scale extraction integration were used in BLSNet to improve the accuracy of lesion segmentation. We compared the performance of the proposed network with that of DeepLabv3+ and UNet as benchmark models used in semantic segmentation. It was found that the proposed BLSNet model demonstrated higher segmentation and class accuracy. A preliminary investigation of BLS disease severity estimation was carried out based on our BLS segmentation results, and it was found that the proposed BLSNet method has strong potential to be a reliable automatic estimator of BLS disease severity.

scholarly journals An Inverse Node Graph-Based Method for the Urban Scene Segmentation of 3D Point Clouds

2021 ◽  
Vol 13 (15) ◽  
pp. 3021
Author(s):  
Bufan Zhao ◽  
Xianghong Hua ◽  
Kegen Yu ◽  
Xiaoxing He ◽  
Weixing Xue ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Point Clouds ◽  
Intelligent Vehicles ◽  
Critical Data ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Cluster Optimization ◽  
Urban Scene ◽  
Processing Steps

Urban object segmentation and classification tasks are critical data processing steps in scene understanding, intelligent vehicles and 3D high-precision maps. Semantic segmentation of 3D point clouds is the foundational step in object recognition. To identify the intersecting objects and improve the accuracy of classification, this paper proposes a segment-based classification method for 3D point clouds. This method firstly divides points into multi-scale supervoxels and groups them by proposed inverse node graph (IN-Graph) construction, which does not need to define prior information about the node, it divides supervoxels by judging the connection state of edges between them. This method reaches minimum global energy by graph cutting, obtains the structural segments as completely as possible, and retains boundaries at the same time. Then, the random forest classifier is utilized for supervised classification. To deal with the mislabeling of scattered fragments, higher-order CRF with small-label cluster optimization is proposed to refine the classification results. Experiments were carried out on mobile laser scan (MLS) point dataset and terrestrial laser scan (TLS) points dataset, and the results show that overall accuracies of 97.57% and 96.39% were obtained in the two datasets. The boundaries of objects were retained well, and the method achieved a good result in the classification of cars and motorcycles. More experimental analyses have verified the advantages of the proposed method and proved the practicability and versatility of the method.

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism

2021 ◽  
pp. 016173462110425
Author(s):  
Jianing Xi ◽  
Jiangang Chen ◽  
Zhao Wang ◽  
Dean Ta ◽  
Bing Lu ◽  
...  
Keyword(s):  
Congenital Anomaly ◽  
Large Scale ◽  
Automatic Segmentation ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Attention Mechanism ◽  
Scale Model ◽  
Ultrasound Images ◽  
Multi Scale ◽  
Task Irrelevant

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in fetal hearts and lungs. To reduce the intensive cost during manual recognition of organ regions, many automatic segmentation methods have been proposed. However, the existing methods still encounter multi-scale problem at a larger range of receptive fields of organs in images, resolution problem of segmentation mask, and interference problem of task-irrelevant features, obscuring the attainment of accurate segmentations. To achieve semantic segmentation with functions of (1) extracting multi-scale features from images, (2) compensating information of high resolution, and (3) eliminating the task-irrelevant features, we propose a multi-scale model with skip connection framework and attention mechanism integrated. The multi-scale feature extraction modules are incorporated with additive attention gate units for irrelevant feature elimination, through a U-Net framework with skip connections for information compensation. The performance of fetal heart and lung segmentation indicates the superiority of our method over the existing deep learning based approaches. Our method also shows competitive performance stability during the task of semantic segmentations, showing a promising contribution on ultrasound based prognosis of congenital anomaly in the early intervention, and alleviating the negative effects caused by congenital anomaly.

Multi-scale Context Intertwining for Semantic Segmentation

2018 ◽  
pp. 622-638 ◽  
Author(s):  
Di Lin ◽  
Yuanfeng Ji ◽  
Dani Lischinski ◽  
Daniel Cohen-Or ◽  
Hui Huang
Keyword(s):  
Semantic Segmentation ◽  
Multi Scale

scholarly journals Semantic Segmentation Network for Surface Defect Detection of Automobile Wheel Hub Fusing High-Resolution Feature and Multi-Scale Feature

2021 ◽  
Vol 11 (22) ◽  
pp. 10508
Author(s):  
Chaowei Tang ◽  
Xinxin Feng ◽  
Haotian Wen ◽  
Xu Zhou ◽  
Yanqing Shao ◽  
...  
Keyword(s):  
High Resolution ◽  
Defect Detection ◽  
Automobile Industry ◽  
Surface Defect ◽  
Semantic Segmentation ◽  
The Body ◽  
Multi Scale ◽  
Surface Defect Detection ◽  
Edge Features ◽  
Automobile Wheel

Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.

scholarly journals Komposisi dan Sebaran Patotipe Xanthomonas oryzae pv. oryzae, Penyakit pada Padi di Nusa Tenggara Barat

2015 ◽  
Vol 34 (2) ◽  
pp. 113
Author(s):  
Sudir Sudir ◽  
Dini Yuliani ◽  
Lalu Wirajaswadi
Keyword(s):  
Disease Severity ◽  
Xanthomonas Oryzae ◽  
Rice Crop ◽  
Bacterial Leaf Blight ◽  
Rice Varieties ◽  
Rice Leaf ◽  
Rice Leaves ◽  
Differential Varieties ◽  
Planting Season ◽  
Screen House

<p>A study was carried out to identify the composition and distribution of Xanthomonas oryzae pv. oryzae (Xoo) pathotypes on rice crop in West Nusa Tenggara, during the 2012 planting season. Three activities were conducted, namely collection of rice leaf samples from the fields, isolation of Xoo from the leaf samples at the laboratory, and testing pathotypes of Xoo at the screen house. Rice leaves showing typical bacterial leaf blight (BLB) symptom were collected from various farmers’ fields. The samples were detached and put into paper envelopes, and were taken to the laboratory for isolation of Xoo, at the Laboratory of Pythopathology of Indonesian Center for Rice Research (ICRR), Sukamandi. Pathotype testing was done in the ICRR screen house by inoculating the leaves of five differential rice varieties using inocula of the Xoo isolates. Resistance of the rice differential varieties was determined based on the BLB disease severity. Inoculated plant with disease severity ≤11% was considered resistant (R) and disease severity &gt;11% was susceptible (S). From the 240 samples of rice leaf infected with BLB collected from West Nusa Tenggara, 232 Xoo isolates were obtained. The Xoo pathotype identification showed that pathotype IV was the most dominant in West Nusa Tenggara during the 2012 planting season, numbering 118 isolates or 51.0% out of the total isolates, followed by pathotype VIII (67 isolates or 29.0%), and pathotype III (47 isolates or 20.0%).</p>

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