Modern Phenotyping: A Paradigm of Present Need in Crop Improvement

References (85)

1. 1 Field high-throughput phenotyping: the new crop breeding frontier [DOI]
2. 2 Phenomics – technologies to relieve the phenotyping bottleneck [DOI]
3. 3 Safeguarding production—losses in major crops and the role of crop protection [DOI]
4. 4 Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping [DOI]
5. 5 Genomic selection in plant breeding: from theory to practice [DOI]
6. 6 A Review of Imaging Techniques for Plant Phenotyping [DOI]
7. 7 Future Scenarios for Plant Phenotyping [DOI]
8. 8 Lights, camera, action: high-throughput plant phenotyping is ready for a close-up [DOI]
9. 9 Field-based phenomics for plant genetics research [DOI]
10. 10 Implementing Precision Agriculture in the 21st Century [DOI]
11. 11 Physiological and Morphological Traits Associated With Spring Wheat Yield Under Hot, Irrigated Conditions [DOI]
12. 12 Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review [DOI]
13. 13 In‐Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance [DOI]
14. 14 Use of Remote‐Sensing Imagery to Estimate Corn Grain Yield [DOI]
15. 15 Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction [DOI]
16. 16 Use of thermal and visible imagery for estimating crop water status of irrigated grapevine [DOI]
17. 17 Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots [DOI]
18. 18 Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping [DOI]
19. 19 Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging [DOI]
20. 20 Recent Weed Control, Weed Management, and Integrated Weed Management [DOI]
21. 21 Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress [DOI]
22. 22 Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System [DOI]
23. 23 Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize [DOI]
24. 24 Spectral Reflectance to Estimate Genetic Variation for In‐Season Biomass, Leaf Chlorophyll, and Canopy Temperature in Wheat [DOI]
25. 25 Spectral signatures of sugar beet leaves for the detection and differentiation of diseases [DOI]
26. 26 Genetic gain in yield and associated changes in phenotype, trait plasticity and competitive ability of South Australian wheat varieties released between 1958 and 2007 [DOI]
27. 27 Drought-adaptive attributes in the Seri/Babax hexaploid wheat population [DOI]
28. 28 Image Analysis: The New Bottleneck in Plant Phenotyping [Applications Corner] [DOI]
29. 29 Concepts of Variable Rate Technology with Considerations for Fertilizer Application [DOI]
30. 30 Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution [DOI]
31. 31 Relationship Between the Normalized SPAD Index and the Nitrogen Nutrition Index: Application to Durum Wheat [DOI]
32. 32 Physiological phenotyping of plants for crop improvement [DOI]
33. 33 Physiological bases of genetic gains in Mediterranean bread wheat yield in Spain [DOI]
34. 34 With ‘Phenomics,’ Plant Scientists Hope to Shift Breeding Into Overdrive [DOI]
35. 35 Canopy temperature variability as an indicator of crop water stress severity [DOI]
36. 36 Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature? [DOI]
37. 37 SPAD Values and Nitrogen Nutrition Index for the Evaluation of Rice Nitrogen Status [DOI]
38. 38 A Flexible, Low‐Cost Cart for Proximal Sensing [DOI]
39. 39 Development and Deployment of a Portable Field Phenotyping Platform [DOI]
40. 40 Breeding-assisted genomics [DOI]
41. 41 Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance [DOI]
42. 42 Field testing of an automatic control system for variable rate fertilizer application [DOI]
43. 43 Crop Row Detection in Maize Fields Inspired on the Human Visual Perception [DOI]
44. 44 Remote Sensing to Estimate Chlorophyll Concentration in Spinach Using Multi-Spectral Plant Reflectance [DOI]
45. 45 Taking transgenic rice drought screening to the field [DOI]
46. 46 Remotely measured canopy temperature of greenhouse strawberries as indicator of water status and yield under mild and very mild water stress conditions [DOI]
47. 47 The use of canopy temperature as an indicator of drought stress in humid regions [DOI]
48. 48 Development of a Model System to Identify Differences in Spring and Winter Oat [DOI]
49. 49 Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images [DOI]
50. 50 Stream grazers determine their crawling direction on the basis of chemical and particulate microalgal cues [DOI]
51. 51 Using Deep Learning for Image-Based Plant Disease Detection [DOI]
52. 52 A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition [DOI]
53. 53 Translating High-Throughput Phenotyping into Genetic Gain [DOI]
54. 54 Deep Learning for Image-Based Cassava Disease Detection [DOI]
55. 55 Plant Phenomics, From Sensors to Knowledge [DOI]
56. 56 Decision support tools for agriculture: Towards effective design and delivery [DOI]
57. 57 Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat [DOI]
58. 58 Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case [DOI]
59. 59 Genomic Selection in Plant Breeding [DOI]
60. 60 Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks [DOI]
61. 61 High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates [DOI]
62. 62 A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding [DOI]
63. 63 Combining High‐Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding [DOI]
64. 64 High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture [DOI]
65. 65 A real-time phenotyping framework using machine learning for plant stress severity rating in soybean [DOI]
66. 66 UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought [DOI]
67. 67 The ETH field phenotyping platform FIP: a cable-suspended multi-sensor system [DOI]
68. 68 Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs) [DOI]
69. 69 Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley [DOI]
70. 70 High-Throughput Phenotyping Enabled Genetic Dissection of Crop Lodging in Wheat [DOI]
71. 71 Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography [DOI]
72. 72 The quest for understanding phenotypic variation via integrated approaches in the field environment [DOI]
73. 73 Gains in Maize Genetic Improvement in Eastern and Southern Africa: I. CIMMYT Hybrid Breeding Pipeline [DOI]
74. 74 Unlocking the potential of plant phenotyping data through integration and data-driven approaches [DOI]
75. 75 Aerial Images and Convolutional Neural Network for Cotton Bloom Detection [DOI]
76. 76 Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging [DOI]
77. 77 Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds [DOI]
78. 78 Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group? [DOI]
79. 79 Enhanced broadband greenness in assessing Chlorophyll a and b, Carotenoid, and Nitrogen in Robusta coffee plantations using a digital camera [DOI]
80. 80 Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat [DOI]
81. 81 Automated identification of sugar beet diseases using smartphones [DOI]
82. 82 A transnational and holistic breeding approach is needed for sustainable wheat production in the Baltic Sea region [DOI]
83. 83 Deploying a Proximal Sensing Cart to Identify Drought-Adaptive Traits in Upland Cotton for High-Throughput Phenotyping [DOI]
84. 84 Specalyzer—an interactive online tool to analyze spectral reflectance measurements [DOI]
85. 85 Agronomy [DOI]

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