2578-2703

The Plant Phenome Journal

Wiley (John Wiley & Sons)

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1

Attach ORCID iDs across 54 articles

ORCID iDs strengthen author disambiguation and institutional reporting.

Low impact54 DOIs
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Low impact33 DOIs

DOIs for this ISSN

Showing the top 5 of 223 DOIs, ordered by correction priority.

# Title Missing Priority Citations
1 Big Data Driven Agriculture: Big Data Analytics in Plant Bre… (10.2135/tppj2018.12.0009)
References ORCID
86.62 53
2 Autonomous Detection of Plant Disease Symptoms Directly from… (10.2135/tppj2019.03.0006)
ORCID
51.52 114
3 Prediction of Maize Grain Yield before Maturity Using Improv… (10.2135/tppj2019.02.0004)
ORCID
47.02 75
4 Temporal Estimates of Crop Growth in Sorghum and Maize Breed… (10.2135/tppj2017.08.0006)
ORCID
44.63 60
5 In‐Field Whole‐Plant Maize Architecture Characterized by Sub… (10.2135/tppj2019.07.0011)
ORCID
41.80 46
6 High‐Throughput Approaches for Phenotyping Alfalfa Germplasm… (10.2135/tppj2019.03.0005)
ORCID
35.78 26
7 Functional Modeling of Plant Growth Dynamics (10.2135/tppj2017.09.0007)
ORCID
35.37 25
8 Carbon Isotope Ratio Fractionation among Plant Tissues of So… (10.2135/tppj2018.04.0002)
ORCID
34.95 24
9 Prediction of Cooking Time for Soaked and Unsoaked Dry Beans… (10.2135/tppj2018.01.0001)
ORCID
33.56 21
10 Systematic approach to validate and implement digital phenot… (10.1002/ppj2.20025)
ORCID
31.97 18
11 Using R‐Based Image Analysis to Quantify Rusts on Perennial… (10.2135/tppj2018.12.0010)
ORCID
30.76 16
12 Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass… (10.2135/tppj2019.02.0003)
ORCID
30.10 15
13 A Low‐Cost Automated System for High‐Throughput Phenotyping… (10.2135/tppj2018.07.0005)
ORCID
26.98 11
14 FIELDimageR.Extra: Advancing user experience and computation… (10.1002/ppj2.20083)
ORCID
22.58 7
15 Time‐Dependent Bias in Instantaneous Ceptometry Caused by Ro… (10.2135/tppj2018.07.0004)
ORCID
22.58 7
16 NAPPN: Who We Are, Where We Are Going, and Why You Should Jo… (10.2135/tppj2018.08.0006)
ORCID
19.45 5
17 Prediction of Soybean Root Response in the Field Using Nonde… (10.2135/tppj2018.04.0003)
ORCID
17.47 4
18 Utilization of Reduced Haploid Vigor for Phenomic Discrimina… (10.2135/tppj2018.10.0008)
ORCID
15.05 3
19 Improving the Precision of NDVI Estimates in Upland Cotton F… (10.2135/tppj2017.09.0009)
ORCID
11.93 2
20 Plot extraction from aerial imagery: A precision agriculture… (10.1002/ppj2.20000)
0.00 6
21 Plant segmentation by supervised machine learning methods (10.1002/ppj2.20001)
0.00 53
22 Phenomic selection and prediction of maize grain yield from… (10.1002/ppj2.20002)
0.00 70
23 High‐throughput measurement of peanut canopy height using di… (10.1002/ppj2.20003)
0.00 37
24 ImageBreed: Open‐access plant breeding web–database for imag… (10.1002/ppj2.20004)
0.00 23
25 FIELDimageR: An R package to analyze orthomosaic images from… (10.1002/ppj2.20005)
0.00 136
26 Thanks to our reviewers (10.1002/ppj2.20006)
Abstract References ORCID
0.00 0
27 Approaches, applications, and future directions for hyperspe… (10.1002/ppj2.20007)
0.00 39
28 Evaluation of a hand‐held spectrophotometer as an in‐field p… (10.1002/ppj2.20008)
0.00 12
29 Nondestructive automated workflow for analyzing diverse leaf… (10.1002/ppj2.20009)
0.00 0
30 Optimization of UAS‐based high‐throughput phenotyping to est… (10.1002/ppj2.20010)
0.00 23
31 Cover Image 1, Volume 3, Issue 1 (10.1002/ppj2.20011)
Abstract References ORCID
0.00 0
32 Making waves in Breedbase: An integrated spectral data stora… (10.1002/ppj2.20012)
0.00 26
33 Utilizing spatial variability from hyperspectral imaging to… (10.1002/ppj2.20013)
0.00 8
34 Picturing the future of food (10.1002/ppj2.20014)
0.00 26
35 <i>PhenoImage</i> : An open‐source graph… (10.1002/ppj2.20015)
0.00 23
36 AC317 <i>The Plant Phenome Journal</i>… (10.1002/ppj2.20016)
Abstract References ORCID
0.00 0
37 Thanks To our Reviewers (10.1002/ppj2.20017)
Abstract References ORCID
0.00 0
38 Optimization of temporal UAS‐based imagery analysis to estim… (10.1002/ppj2.20018)
0.00 26
39 Measuring canopy height in soybean and wheat using a low‐cos… (10.1002/ppj2.20019)
0.00 9
40 How useful is active learning for image‐based plant phenotyp… (10.1002/ppj2.20020)
0.00 27
41 Three‐channel electrical impedance spectroscopy for field‐sc… (10.1002/ppj2.20021)
0.00 21
42 Automation of leaf counting in maize and sorghum using deep… (10.1002/ppj2.20022)
0.00 37
43 Images carried before the fire: The power, promise, and resp… (10.1002/ppj2.20023)
0.00 19
44 Prospector: A mobile application for portable, high‐throughp… (10.1002/ppj2.20024)
0.00 5
45 Comparison of image georeferencing strategies for agricultur… (10.1002/ppj2.20026)
0.00 12
46 Phenomic selection is competitive with genomic selection for… (10.1002/ppj2.20027)
0.00 63
47 Bioenergy Underground: Challenges and opportunities for phen… (10.1002/ppj2.20028)
0.00 22
48 Cost‐effective, high‐throughput phenotyping system for 3D re… (10.1002/ppj2.20029)
0.00 19
49 A UAV‐based high‐throughput phenotyping approach to assess t… (10.1002/ppj2.20030)
0.00 43
50 QScout: A QGIS plugin tool suite for georeferencing and anal… (10.1002/ppj2.20031)
0.00 2