Construction, implementation and testing of an image identification system using computer vision methods for fruit flies with economic importance (Diptera: Tephritidae)

2016 ◽  
Vol 73 (7) ◽  
pp. 1511-1528 ◽  
Author(s):  
Jiang-ning Wang ◽  
Xiao-lin Chen ◽  
Xin-wen Hou ◽  
Li-bing Zhou ◽  
Chao-Dong Zhu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Fruit Flies ◽  
Economic Importance ◽  
Identification System ◽  
Image Identification

An intelligent identification system combining image and DNA sequence methods for fruit flies with economic importance (Diptera: Tephritidae)

2021 ◽  
Author(s):  
Jiangning Wang ◽  
Yingying Chen ◽  
Xinwen Hou ◽  
Yong Wang ◽  
Libing Zhou ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Fruit Flies ◽  
Economic Importance ◽  
Identification System

scholarly journals Biological Control of Tephritid Fruit Flies in the Americas and Hawaii: A Review of the Use of Parasitoids and Predators

Insects ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 662
Author(s):  
Flávio R. M. Garcia ◽  
Sérgio M. Ovruski ◽  
Lorena Suárez ◽  
Jorge Cancino ◽  
Oscar E. Liburd
Keyword(s):  
Biological Control ◽  
Fruit Fly ◽  
Fruit Flies ◽  
Economic Importance ◽  
Parasitoid Species ◽  
Control Programs ◽  
First Time ◽  
Tephritid Fruit Flies

Biological control has been the most commonly researched control tactic within fruit fly management programs. For the first time, a review is carried out covering parasitoids and predators of fruit flies (Tephritidae) from the Americas and Hawaii, presenting the main biological control programs in this region. In this work, 31 species of fruit flies of economic importance are considered in the genera Anastrepha (11), Rhagoletis (14), Bactrocera (4), Ceratitis (1), and Zeugodacus (1). In this study, a total of 79 parasitoid species of fruit flies of economic importance are listed and, from these, 50 are native and 29 are introduced. A total of 56 species of fruit fly predators occur in the Americas and Hawaii.

Host Plants of Fruit Flies (Diptera: Tephritidae) of Economic Importance in Guatemala

1987 ◽  
Vol 70 (1) ◽  
pp. 116 ◽  
Author(s):  
Fred M. Eskafi ◽  
Roy T. Cunningham
Keyword(s):  
Host Plants ◽  
Fruit Flies ◽  
Economic Importance

Demography of Anastrepha Fruit Flies: A Case Study of Three Species of Economic Importance

Fruit Flies ◽  
1993 ◽  
pp. 119-124
Author(s):  
P. Liedo ◽  
J. R. Carey ◽  
H. Celedonio ◽  
J. Guillen
Keyword(s):  
Fruit Flies ◽  
Economic Importance

Validating an automated image identification process of a passive image-assisted dietary assessment method: proof of concept

2020 ◽  
Vol 23 (15) ◽  
pp. 2700-2710
Author(s):  
Tsz-Kiu Chui ◽  
Jindong Tan ◽  
Yan Li ◽  
Hollie A. Raynor
Keyword(s):  
Dietary Assessment ◽  
False Negative ◽  
Assessment Method ◽  
Identification Accuracy ◽  
Identification System ◽  
True Negative ◽  
Image Identification ◽  
Effect Of Food ◽  
Food Image ◽  
Food Images

AbstractObjective:To validate an automated food image identification system, DietCam, which has not been validated, in identifying foods with different shapes and complexities from passively taken digital images.Design:Participants wore Sony SmartEyeglass that automatically took three images per second, while two meals containing four foods, representing regular- (i.e., cookies) and irregular-shaped (i.e., chips) foods and single (i.e., grapes) and complex (i.e., chicken and rice) foods, were consumed. Non-blurry images from the meals’ first 5 min were coded by human raters and compared with DietCam results. Comparisons produced four outcomes: true positive (rater/DietCam reports yes for food), false positive (rater reports no food; DietCam reports food), true negative (rater/DietCam reports no food) or false negative (rater reports food; DietCam reports no food).Setting:Laboratory meal.Participants:Thirty men and women (25·1 ± 6·6 years, 22·7 ± 1·6 kg/m2, 46·7 % White).Results:Identification accuracy was 81·2 and 79·7 % in meals A and B, respectively (food and non-food images) and 78·7 and 77·5 % in meals A and B, respectively (food images only). For food images only, no effect of food shape or complexity was found. When different types of images, such as 100 % food in the image and on the plate, <100 % food in the image and on the plate and food not on the plate, were analysed separately, images with food on the plate had a slightly higher accuracy.Conclusions:DietCam shows promise in automated food image identification, and DietCam is most accurate when images show food on the plate.

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