Defoliation patterns and genetics of insect resistance in cottonwoods

In 1995, an outbreak of a leaf beetle, Phratora californica Brown (Coleoptera: Chrysomelidae), began in a three-generation Populus trichocarpa Torr. & Gray × Populus deltoides Bartr. pedigree planting near the lower Columbia River in Oregon. This outbreak provided us with an opportunity to assess leaf beetle feeding patterns and the genetics of cottonwood resistance to defoliation. We developed a method for estimating damage levels by training personnel to visually estimate percent damage in leaf samples. Digital image analysis was used to measure damage to the leaves used in the training. Based on a sample of 300 trees from 100 genotypes, herbivory was found to be greatest in the upper canopy and in the fall. Broad-sense heritability was estimated to be 0.88 and 0.80 for July and October, respectively, demonstrating that resistance to P. californica is under relatively strong genetic control. Resistance in the F2 likely came from the P. trichocarpa parent, because this parent was less susceptible, on average, than the P. deltoides parent. However, the difference between parents was not great, and any further genetic analysis of resistance to Phratora californica should employ crosses between individuals with more strongly contrasting phenotypes.

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A method for measuring the residence time distribution of particles in a fluidized bed based on digital image analysis

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2020-0177 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Jiamin Li ◽

Xiaoping Chen ◽

Jiliang Ma ◽

Cai Liang

Keyword(s):

Image Analysis ◽

Fluidized Bed ◽

Residence Time ◽

Digital Image ◽

Time Distribution ◽

Residence Time Distribution ◽

Digital Image Analysis ◽

Color Space ◽

The Difference ◽

Accuracy And Repeatability

AbstractTraditional methods for measuring the residence time distribution (RTD) of particles in a fluidized bed are complex and time-consuming. To this regard, the present work proposes a new measurement method with remarkable efficiency based on digital image analysis. The dyed tracers are recognized in the images of the samples due to the difference of colors from bed materials. The HSV and the well-known RGB color space were employed to distinguish the tracers. By enhancing the Saturation and the Value in HSV and adjusting the gray range of images, the recognition error is effectively reduced. Then the pixels representing the tracers are distinguished, based on which the concentration of the tracers and RTD are measured. The efficiency, accuracy and repeatability of the method were validated by RTD measurements experiments. The method is also fit for distinguishing the target particles from multi-component systems consisting of particles of different colors.

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Accuracy of body measurements using digital image analysis in female Holstein calves

Animal Production Science ◽

10.1071/an12006 ◽

2012 ◽

Vol 52 (10) ◽

pp. 917 ◽

Cited By ~ 6

Author(s):

Serkan Ozkaya

Keyword(s):

Image Analysis ◽

Body Length ◽

Digital Image ◽

Digital Image Analysis ◽

Digital Camera ◽

Body Measurements ◽

Analysis Software ◽

High Prediction ◽

Holstein Calves ◽

The Difference

The objective of this study was to determine the accuracy of body measurements (BM) in Holstein female calves using digital image analysis. BM including body length, wither height, chest depth, hip height, and hip width of calves were recorded by stick and tape measurements at birth, weaning and 24 weeks of age. Then photos of calves were taken while calves were standing in a squeeze chute by a digital camera and were analysed by image analysis software to obtain BM of each calf from the image in centimetres. After comparing the actual and predicted BM, the accuracy was determined as 71, 97 and 99% for body length, 69, 87 and 99% for wither height, 43, 98 and 99% for chest depth, 74, 99 and 99% for hip height and 53, 99 and 98% for hip width at birth, weaning and 24 weeks of age, respectively. The difference between actual and predicted BM was significant at birth (P < 0.01). Although there were numerical differences between actual and predicted BM, the differences were not significant at weaning and 24 weeks of age (P > 0.01). According to these results, the BM estimation of Holstein female calves using digital image analysis produced high prediction accuracy at weaning and 24 weeks of age, but not at birth. The data presented in this study indicate that the digital image analysis provides very close agreement and reality for prediction of BM of Holstein female calves.

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