| Full text | |
| Author(s): |
da Silva, Clissia Barboza
[1]
;
Naves Silva, Alysson Alexander
[2]
;
Barroso, Geovanny
[3]
;
Yamamoto, Pedro Takao
[3]
;
Arthur, Valter
[1]
;
Motta Toledo, Claudio Fabiano
[2]
;
Mastrangelo, Thiago de Araujo
[1]
Total Authors: 7
|
| Affiliation: | [1] Univ Sao Paulo, Ctr Nucl Energy Agr, BR-13416000 Piracicaba, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, BR-13560970 Sao Carlos, SP - Brazil
[3] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Entomol & Acarol, BR-13418900 Piracicaba, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | FOODS; v. 10, n. 4 APR 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (>= 0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection. (AU) | |
| FAPESP's process: | 18/01774-3 - Non-destructive image analysis methods for seed quality evaluation |
| Grantee: | Clíssia Barboza Mastrangelo |
| Support Opportunities: | Scholarships in Brazil - Young Researchers |
| FAPESP's process: | 18/03793-5 - Multi-user equipment approved in grant 2017/15220-7: imaging system SeedReporter camera spectral & colour |
| Grantee: | Clíssia Barboza Mastrangelo |
| Support Opportunities: | Multi-user Equipment Program |
| FAPESP's process: | 18/03807-6 - Multi-user equipment approved in grant 2017/15220-7: multiFocus digital radiography system |
| Grantee: | Clíssia Barboza Mastrangelo |
| Support Opportunities: | Multi-user Equipment Program |
| FAPESP's process: | 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation |
| Grantee: | Clíssia Barboza Mastrangelo |
| Support Opportunities: | Research Grants - Young Investigators Grants |