scholarly journals An Instance Segmentation-Based Method to Obtain the Leaf Age and Plant Centre of Weeds in Complex Field Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3389
Author(s):  
Longzhe Quan ◽  
Bing Wu ◽  
Shouren Mao ◽  
Chunjie Yang ◽  
Hengda Li
Keyword(s):  
Leaf Age ◽  
Model Performance ◽  
Morphological Structure ◽  
Complex Field ◽  
Accurate Identification ◽  
Backbone Networks ◽  
Phenotypic Information ◽  
Improve Model ◽  
Improve Model Performance ◽  
Top View

Leaf age and plant centre are important phenotypic information of weeds, and accurate identification of them plays an important role in understanding the morphological structure of weeds, guiding precise targeted spraying and reducing the use of herbicides. In this work, a weed segmentation method based on BlendMask is proposed to obtain the phenotypic information of weeds under complex field conditions. This study collected images from different angles (front, side, and top views) of three kinds of weeds (Solanum nigrum, barnyard grass (Echinochloa crus-galli), and Abutilon theophrasti Medicus) in a maize field. Two datasets (with and without data enhancement) and two backbone networks (ResNet50 and ResNet101) were replaced to improve model performance. Finally, seven evaluation indicators are used to evaluate the segmentation results of the model under different angles. The results indicated that data enhancement and ResNet101 as the backbone network could enhance the model performance. The F1 value of the plant centre is 0.9330, and the recognition accuracy of leaf age can reach 0.957. The mIOU value of the top view is 0.642. Therefore, deep learning methods can effectively identify weed leaf age and plant centre, which is of great significance for variable spraying.

scholarly journals Instance segmentation based method to obtain the phenotypic information of weeds in complex field environments

2020 ◽  
Author(s):  
Quan longzhe ◽  
Bing Wu ◽  
Shou ren Mao ◽  
Huaiqu Feng ◽  
Chunjie Yang ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Leaf Age ◽  
Model Performance ◽  
Morphological Structure ◽  
Field Conditions ◽  
Detection Accuracy ◽  
Complex Field ◽  
Phenotypic Information ◽  
Top View ◽  
Instance Segmentation

Abstract Background: Weeds pose a critical threat to crop growth. The leaf age and plant centre, which represent the key phenotypic information of weeds, can help understand the morphological structure of weeds, thereby facilitating precise targeted spraying and a reduction in the herbicide usage. However, determining the weed types, leaf age and plant centre under complex field conditions involving variations in the light and plant appearance along with leaf occlusion is challenging. With the advancement in the application of deep learning with computer vision, such approaches can likely overcome these challenges, as demonstrated in other complex agricultural applications.Results: We developed a weed segmentation method based on BlendMask, which could obtain the weed types, leaf age and plant centre under complex field conditions. Mobile devices were used to capture digital images at different angles (front, side, and top views) of certain weeds (Solanum nigrum, Barnyard grass, and Abutilon theophrasti Medicus) in the field. Subsequently, two datasets (with and without data enhancement) were produced and input to the network. Moreover, two backbone networks, ResNet50 and ResNet101, were compared, along with six instance segmentation algorithms, and the instance segmentation results of the model under different angles were evaluated. The results indicated that data enhancement could enhance the model performance. In the case with data enhancement, the F1 value, AP50 and AP70 scores, and mIOU with ResNet101 as the backbone network were 0.9479, 0.720, 0.592, and 0.607, respectively, corresponding to the highest segmentation performance. Furthermore, the top view images of the weeds corresponded to the highest detection accuracy, compared to that for the other two angles.Conclusion: BlendMask can realize accurate segmentation of weeds to obtain the types, leaf age and plant centre of weeds. Data enhancement and use of the weed image corresponding to the top view angle can help enhance the model performance. The dataset and research results can provide guidance to further develop precision agriculture practices.

scholarly journals Method for Obtaining Phenotypic Traits of Weeds in Complex Field Environments Based on Instance Segmentation

2020 ◽  
Author(s):  
Quan longzhe ◽  
Bing Wu ◽  
Shou ren Mao ◽  
Huaiqu Feng ◽  
Chunjie Yang ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Leaf Age ◽  
Central Area ◽  
Morphological Structure ◽  
Field Conditions ◽  
Phenotypic Traits ◽  
Detection Accuracy ◽  
Complex Field ◽  
Top View ◽  
Instance Segmentation

Abstract Background: Weeds are the biggest threat to crop growth, and leaf age and central area of weeds are important phenotypic traits of weeds. They have an important role in understanding the morphological structure of weeds, guiding precision for target weeding and reducing the use of herbicides. However, it is still a substantial challenge to obtain weed types, leaf age and central area in the complex field conditions of light changes, variation in appearance of plants, leaf occlusion. The latest developments in deep learning provide new tools for solving challenging computer vision tasks.Results: In this study, we present a weed phenotype segmentation method based on Mask R-CNN that obtains weed types, leaf age and central area in the complex field conditions. By shooting three different angles of the main weeds (Solanum nigrum, Barnyard grass, and Abutilon theophrasti Medicus) in the field through mobile devices, two datasets (data enhancement and without data enhancement) were produced and used as input to the network, two backbone networks was tested, namely ResNet50 and ResNet101, and the detection results and instance segmentation results of the model were evaluated. The results showed that data enhancement can improve the performance of the model. In the case of data enhancement, the F1 value with ResNet101 as the backbone network was 0.9214, the mAP scores were 0.6932 and 0.5244 (for IOU thresholds of 0.5 and 0.7, respectively), the mIOU reached 0.585, and the best segmentation performance example was obtained. Furthermore, the weed image taken from the top view angle compared to the other two angles achieved the highest detection accuracy.Conclusion: Mask R-CNN can achieve accurate segmentation of weeds to obtain the types, leaf age and central area of weeds. Data enhancement and the weed image taken from the top view angle can help to improve the performance of the model. This dataset and research results may provide important resources for the development of precision agriculture in the future.

scholarly journals Towards Robust Representations of Spatial Networks Using Graph Neural Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6918
Author(s):  
Chidubem Iddianozie ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Model Performance ◽  
Network Models ◽  
Data Representation ◽  
Spatial Networks ◽  
Neural Network Models ◽  
Improve Model ◽  
Graph Neural Networks ◽  
Spatial Entities ◽  
Improve Model Performance

The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogeneous representation may benefit spatial networks and could improve model performance on certain tasks. Thus, we carry out an empirical study using Graph Neural Network models for two inference tasks on spatial networks. Our results demonstrate that heterogeneous representations improves model performance for down-stream inference tasks on spatial networks.

scholarly journals Population Pharmacokinetics of Doxycycline in Children

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Elizabeth J. Thompson ◽  
Huali Wu ◽  
Chiara Melloni ◽  
Stephen Balevic ◽  
Janice E. Sullivan ◽  
...  
Keyword(s):  
Model Performance ◽  
Standard Of Care ◽  
Volume Of Distribution ◽  
Clinical Value ◽  
Final Model ◽  
Childhood Infections ◽  
Obesity Status ◽  
Improve Model ◽  
Using Data ◽  
Improve Model Performance

ABSTRACT Doxycycline is a tetracycline-class antimicrobial labeled by the U.S. Food and Drug Administration for children >8 years of age for many common childhood infections. Doxycycline is not labeled for children ≤8 years of age, due to the association between tetracycline-class antibiotics and tooth staining, although doxycycline may be used off-label under severe conditions. Accordingly, there is a paucity of pharmacokinetic (PK) data to guide dosing in children 8 years and younger. We leveraged opportunistically collected plasma samples after intravenous (i.v.) and oral doxycycline doses received per standard of care to characterize the PK of doxycycline in children of different ages and evaluated the effect of obesity and fasting status on PK parameters. We developed a population PK model of doxycycline using data collected from 47 patients 0 to 18 years of age, including 14 participants ≤8 years. We developed a 1-compartment PK model and found doxycycline clearance to be 3.32 liters/h/70 kg of body weight and volume to be 96.8 liters/70 kg for all patients, comparable to values reported in adults. We estimated a bioavailability of 89.6%, also consistent with adult data. Allometrically scaled clearance and volume of distribution did not differ between children 2 to ≤8 years of age and children >8 to ≤18 years of age, suggesting that younger children may be given the same per-kilogram dosing. Obesity status and fasting status were not selected for inclusion in the final model. Additional doxycycline PK samples collected in future studies may be used to improve model performance and maximize its clinical value.

scholarly journals Improving biomedical named entity recognition with syntactic information

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanhe Tian ◽  
Wang Shen ◽  
Yan Song ◽  
Fei Xia ◽  
Min He ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Model Performance ◽  
Entity Recognition ◽  
Input Word ◽  
Named Entity ◽  
Improve Model ◽  
Syntactic Information ◽  
Biomedical Texts ◽  
Improve Model Performance ◽  
Biomedical Named Entity Recognition

Abstract Background Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance. Results In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800). Conclusion The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance

2021 ◽  
Vol 268 ◽  
pp. 115951
Author(s):  
Xiangyu Xu ◽  
Ning Qin ◽  
Zhenchun Yang ◽  
Yunwei Liu ◽  
Suzhen Cao ◽  
...  
Keyword(s):  
Model Performance ◽  
Mobile Monitoring ◽  
Short Term ◽  
Improve Model ◽  
Improve Model Performance

scholarly journals Probing Behavior of Diaphorina citri (Hemiptera: Liviidae) on Valencia Orange Influenced by Sex, Color, and Size

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Timothy A Ebert ◽  
Michael E Rogers
Keyword(s):  
Body Size ◽  
Model Performance ◽  
Diaphorina Citri ◽  
Probing Behavior ◽  
Valencia Orange ◽  
Improve Model ◽  
Candidatus Liberibacter ◽  
Citrus Huanglongbing ◽  
Liberibacter Asiaticus ◽  
Improve Model Performance

Abstract Candidatus Liberibacter asiaticus Jagoueix, Bové, and Garnier (Rhizobiales: Rhizobiaceae) is transmitted by the psyllid Diaphorina citri Kuwayama and putatively causes Huanglongbing disease in citrus. Huanglongbing has reduced yields by 68% relative to pre-disease yields in Florida. Disease management is partly through vector control. Understanding vector biology is essential in this endeavor. Our goal was to document differences in probing behavior linked to sex. Based on both a literature review and our results, we conclude that there is either no effect of sex or that identifying such an effect requires a sample size at least four times larger than standard methodologies. Including both color and sex in statistical models did not improve model performance. Both sex and color are correlated with body size, and body size has not been considered in previous studies on sex in D. citri in terms of probing behavior. An effect of body size was found wherein larger psyllids took longer to reach ingestion behaviors and larger individuals spent more time-ingesting phloem, but these relationships explained little of the variability in these data. We suggest that the effects of sex can be ignored when running EPG experiments on healthy psyllids.

scholarly journals Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

2021 ◽  
Author(s):  
Roshan A. Karunamuni ◽  
Minh-Phuong Huynh-Le ◽  
Chun C. Fan ◽  
Wesley Thompson ◽  
Asona Lui ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
African Ancestry ◽  
Prostate Cancer Risk ◽  
Model Performance ◽  
Principal Component ◽  
Cox Proportional Hazards ◽  
Risk Scores ◽  
Improve Model ◽  
Improve Model Performance ◽  
Improve Health

AbstractWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ. Genotypic data were obtained from PRACTICAL consortium for 6,253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with performance of PHS46+African. A calibration factor (CF) was formulated using estimated Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC. CF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our dataset with 1000 Genomes, we identified statistically significant associations between continental and calibration groupings. In conclusion, we identified PCs within 8q24 SNP window that were strongly associated with performance of PHS46+African. Further research to improve clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry

scholarly journals A Process-Oriented Model for Simulating Avalanche Danger

1980 ◽  
Vol 26 (94) ◽  
pp. 53-63
Author(s):  
Arthur Judson ◽  
Charles F. Leaf ◽  
Glen E. Brink
Keyword(s):  
Sensitivity Analysis ◽  
Snow Depth ◽  
Process Model ◽  
Model Performance ◽  
Simulation Process ◽  
Improve Model ◽  
Westerly Winds ◽  
Improve Model Performance ◽  
Snow Transport ◽  
Avalanche Danger

AbstractA simulation process model is developed for rating avalanche danger for twelve east-facing avalanche paths loaded by westerly winds. The model simulates layer age and densification, snow depth, snow transport and deposition, formation of melt crusts, snow temperatures, temperature gradient metamorphism, and avalanche danger on a 6 h basis. Conditioned on avalanches alone, the model predicted avalanche potential on 86% of the 175 avalanche days during an eight-year period. It indicated avalanche potential 50% of the time on non-avalanche days. A sensitivity analysis is under way to improve model performance, and simulation of danger from additional avalanche samples is planned.

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