X-ray image analysis to detect infestation due to Cryptolestes ferrrugineus in stored wheat

Canadian Conference on Electrical and Computer Engineering

Volumn 2, Issue , 2002, Pages 902-907

  Karunakaran, C ^a   Jayas, Digvir S ^a   White, N D G ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

Histogram and textural features; Infestation detection; Soft X ray

Indexed keywords

BACKPROPAGATION; FEATURE EXTRACTION; FEEDFORWARD NEURAL NETWORKS; FOOD STORAGE; GRAIN (AGRICULTURAL PRODUCT); INSECT CONTROL; MULTILAYER NEURAL NETWORKS; X RAYS;

HISTOGRAMS; SOFT X RAYS;

IMAGE ANALYSIS;

FOOD STORAGE; IMAGE ANALYSIS; INFESTATION; NEURAL NETWORKS; WHEAT; X RAYS;

EID: 0036062883     PISSN: 08407789     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Efficiency of Berlese-Tullgren funnels for removal of the rusty grain beetle, cryptolestes ferrugineus, from wheat samples
2. Annual costs related to stored-product pests in Canada (losses and preventive measures)
3. Nuclear magnetic resonance spectroscopy for studying the development and detection of the grain weevil, Sitophilus granarius (L.) (Coleoptera: Curculionidae), within wheat kernels
4. Automated monitoring using acoustical sensors for insects in farm-stored wheat
5. Identifying stored-grain insects using near-infrared spectroscopy
6. Radiography applied to grain and seeds
7. Automated X-ray inspection of grain for insect infestation
8. Analysis of radiograms of wheat kernels for quality control
9. X-ray inspection of wheat for granary weevils
10. Detection of insect infestations in wheat kernels using soft X-rays
11. Application of X-ray technique to the detection of internal insect infestation of grain
12. Effect of image resolution on insect detection in wheat radiographs
13. Machine recognition of weevil damage in wheat radiographs
14. Textural analysis using gray level run lengths
15. Statistical and structural approaches to texture
16. Sum and difference histograms for texture classification
17. Classification of cereal grains using machine vision: III. Texture models
18. Multi-layer neural networks for image analysis of agricultural products
19. Evaluation of neural network architectures for cereal grain classification using morphological features
20. Comparison of statistical and neural network methods for the classification of cereal grains using machine vision systems