scholarly journals A Semi-Automatic Workflow to Extract Irregularly Aligned Plots and Sub-Plots: A Case Study on Lentil Breeding Populations

2021 ◽  
Vol 13 (24) ◽  
pp. 4997
Author(s):  
Thuan Ha ◽  
Hema Duddu ◽  
Kirstin Bett ◽  
Steve J. Shirtliffe
Keyword(s):  
Field Experiments ◽  
Accuracy Assessment ◽  
Phenotypic Data ◽  
Local Maxima ◽  
Breeding Populations ◽  
Convolution Filter ◽  
Data Points ◽  
Phenotyping Experiment ◽  
Green Index

Plant breeding experiments typically contain a large number of plots, and obtaining phenotypic data is an integral part of most studies. Image-based plot-level measurements may not always produce adequate precision and will require sub-plot measurements. To perform image analysis on individual sub-plots, they must be segmented from plots, other sub-plots, and surrounding soil or vegetation. This study aims to introduce a semi-automatic workflow to segment irregularly aligned plots and sub-plots in breeding populations. Imagery from a replicated lentil diversity panel phenotyping experiment with 324 populations was used for this study. Image-based techniques using a convolution filter on an excess green index (ExG) were used to enhance and highlight plot rows and, thus, locate the plot center. Multi-threshold and watershed segmentation were then combined to separate plants, ground, and sub-plot within plots. Algorithms of local maxima and pixel resizing with surface tension parameters were used to detect the centers of sub-plots. A total of 3489 reference data points was collected on 30 random plots for accuracy assessment. It was found that all plots and sub-plots were successfully extracted with an overall plot extraction accuracy of 92%. Our methodology addressed some common issues related to plot segmentation, such as plot alignment and overlapping canopies in the field experiments. The ability to segment and extract phenometric information at the sub-plot level provides opportunities to improve the precision of image-based phenotypic measurements at field-scale.

scholarly journals QuickBird image-based estimation of tree stand density using local maxima filtering method: A case study in a Beijing forest

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0208256
Author(s):  
Shuhan Wang ◽  
Xiaoli Zhang ◽  
Mohammed Abdelmanan Hassan ◽  
Qi Chen ◽  
Chaokui Li ◽  
...  
Keyword(s):  
Stand Density ◽  
Tree Stand ◽  
Local Maxima ◽  
Filtering Method

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

2018 ◽  
Vol 41 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Rebecca Campbell ◽  
Rachael Goodman-Williams ◽  
Hannah Feeney ◽  
Giannina Fehler-Cabral
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Policy Makers ◽  
Specific Data ◽  
Qualitative And Quantitative ◽  
Stakeholder Groups ◽  
Data Points ◽  
Evaluation Project ◽  
Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

scholarly journals A Robust Noise Resistant Algorithm for POI Identification from Flickr Data

2017 ◽  
Author(s):  
Yiyang Yang ◽  
Zhiguo Gong ◽  
Qing Li ◽  
Leong Hou U ◽  
Ruichu Cai ◽  
...  
Keyword(s):  
Social Media ◽  
Joint Space ◽  
Zero Crossing ◽  
Social Media Data ◽  
Local Maxima ◽  
Gradient Ascent ◽  
Data Density ◽  
Density Values ◽  
Data Points ◽  
Media Data

Point of Interests (POI) identification using social media data (e.g. Flickr, Microblog) is one of the most popular research topics in recent years. However, there exist large amounts of noises (POI irrelevant data) in such crowd-contributed collections. Traditional solutions to this problem is to set a global density threshold and remove the data point as noise if its density is lower than the threshold. However, the density values vary significantly among POIs. As the result, some POIs with relatively lower density could not be identified. To solve the problem, we propose a technique based on the local drastic changes of the data density. First we define the local maxima of the density function as the Urban POIs, and the gradient ascent algorithm is exploited to assign data points into different clusters. To remove noises, we incorporate the Laplacian Zero-Crossing points along the gradient ascent process as the boundaries of the POI. Points located outside the POI region are regarded as noises. Then the technique is extended into the geographical and textual joint space so that it can make use of the heterogeneous features of social media. The experimental results show the significance of the proposed approach in removing noises.

scholarly journals Coupling Uncertainties with Accuracy Assessment in Object-based Slum Detections, Case Study: Jakarta, Indonesia

2017 ◽  
Author(s):  
Jati Pratomo ◽  
Monika Kuffer ◽  
Javier Martinez ◽  
Divyani Kohli
Keyword(s):  
Land Tenure ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
False Negative ◽  
National Policy ◽  
True Positive ◽  
True Negative ◽  
Object Based ◽  
The Impact

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

scholarly journals Potassium impact on nitrogen use efficiency in potato – a case study from the Central-East Europe

2017 ◽  
Vol 63 (No. 9) ◽  
pp. 422-427 ◽  
Author(s):  
GRZEBISZ Witold ◽  
ČERMÁK Pavel ◽  
RROCO Evan ◽  
SZCZEPANIAK Witold ◽  
POTARZYCKI Jarosław ◽  
...  
Keyword(s):  
Field Experiments ◽  
Climatic Conditions ◽  
Potato Yield ◽  
Nitrogen Efficiency ◽  
East Europe ◽  
Application Management ◽  
Application Rates ◽  
Potassium Efficiency ◽  
Use Efficiency

Potato yield is affected by an interaction between nitrogen (N) and potassium (K) supply. This hypothesis was verified in a series of field experiments conducted during 2010–2013 in Albania (AL), Czech Republic (CZ) and Poland (PL). The two-factorial experiment was founded on relative scales of K (0, 50, 100, and 150%), and N application rates (75% and 100%) of the recommended doses, which were country-specific. The average tuber yield was doubled for AL, increased by 50% for PL, and by 15% for the CZ in response to K and N interaction. These differences are caused by an increase in the apparent nitrogen efficiency (ANE), which rose significantly by the progressive Krates. Maximum average ANE of 90 kg tubers/kg N was recorded in AL; it was 2-fold lower in CZ. Top average apparent potassium efficiency (AKE) of 65 kg tubers/kg K was recorded in PL; it was 4-times lower in CZ. The relationships between AKE and ANE clearly demonstrate the tight interaction between the N and K, and its effects on potato yield. However, a sound K application management should be adjusted to the local edaphic and climatic conditions.

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