Escape of Lygus hesperus (Heteroptera: Miridae) Eggs from Parasitism by Anaphes iole (Hymenoptera: Mymaridae) in Strawberries: Plant Structure Effects

2000 ◽  
Vol 17 (3) ◽  
pp. 234-242 ◽  
Author(s):  
Sujaya Udayagiri ◽  
Stephen C Welter
Keyword(s):  
Plant Structure ◽  
Lygus Hesperus ◽  
Anaphes Iole

REDESCRIPTION OF AND HOST-INDUCED ANTENNAL VARIATION IN ANAPHES IOLE GIRAULT (HYMENOPTERA: MYMARIDAE), AN EGG PARASITE OF MIRIDAE (HEMIPTERA) IN NORTH AMERICA

1988 ◽  
Vol 120 (10) ◽  
pp. 893-901 ◽  
Author(s):  
John T. Huber ◽  
V.K. Rajakulendran
Keyword(s):  
North America ◽  
Morphological Variation ◽  
Relative Size ◽  
Structural Features ◽  
Lygus Hesperus ◽  
Egg Parasite ◽  
Anaphes Iole

AbstractAnaphes iole Girault is redescribed from specimens reared from Lygus hesperus Knight and Pseudatomoscelis seriatus (Reuter). Anaphes anomocerus, A. ovijentatus, and A. perdubius are synonyms of A. iole. Host-induced morphological variation is discussed. The main attributes that vary with host are the relative size of certain funicular articles and their complement of sensory ridges. There is no morphological overlap between populations as indicated by measurements of most other structures. However, certain structural features that can be used to define the species unequivocally were found.

Parasitism of Lygus hesperus (Miridae) by Anaphes iole (Mymaridae) and Leiophron uniformis (Braconidae) in California strawberry

1992 ◽  
Vol 2 (2) ◽  
pp. 131-137 ◽  
Author(s):  
Andrew P. Norton ◽  
Stephen C. Welter ◽  
J.Lindsey Flexner ◽  
C.G. Jackson ◽  
Jack W. Debolt ◽  
...  
Keyword(s):  
Lygus Hesperus ◽  
Anaphes Iole

Superparasitism of Lygus hesperus Knight Eggs by Anaphes iole Girault in the Laboratory

2002 ◽  
Vol 23 (2) ◽  
pp. 121-126 ◽  
Author(s):  
Z.X Wu ◽  
Donald A Nordlund
Keyword(s):  
Lygus Hesperus ◽  
Anaphes Iole

Plant Structure: Function and Development

1993 ◽  
Author(s):  
John A. Romberger ◽  
Zygmunt Hejnowicz ◽  
Jane F. Hill
Keyword(s):  
Structure Function ◽  
Plant Structure

scholarly journals Label3DMaize: toolkit for 3D point cloud data annotation of maize shoots

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Training Dataset ◽  
Growth Stages ◽  
3D Point Cloud ◽  
Plant Structure ◽  
Data Annotation ◽  
Cloud Data ◽  
Point Cloud Segmentation ◽  
Different Growth Stages

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.

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