Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture
Spilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for ide...
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IEEE
2024-01-01
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| author | Saul Huaquipaco Oscar Vera Victor Yana-Mamani Wilson Mamani Helarf Calsina Flavio Puma Eli Morales-Rojas Norman Beltran Jose Cruz |
| author_facet | Saul Huaquipaco Oscar Vera Victor Yana-Mamani Wilson Mamani Helarf Calsina Flavio Puma Eli Morales-Rojas Norman Beltran Jose Cruz |
| author_sort | Saul Huaquipaco |
| collection | DOAJ |
| description | Spilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for identifying peacock spots on olive leaves. This new meta-architecture integrates Xception and VGG16 as the basis for the methodology employed in this study. Furthermore, a machine learning approach was used to pretrain the model in an unsupervised manner, thereby improving its generalization capacity. Metrics such as Kappa (K), True Skill Statistic (TSS), Proportion of Expected Success (Pe), Threat Index (Ts), and the Heidke Skill Score (HSS) were used to validate the model results. Based on these indicators, we evaluated the robustness, accuracy, and ability of the model to identify peacock spots. The testing results showed that the suggested meta-architecture, named SSL-XceVNet, substantially outperformed the baseline XceVNet model (88.24%) in detecting peacock spots, with an accuracy of 95.22%. The validation measurement results (K, 73.57%; TSS, 72.66%; Pe, 55.06%; Ts, 90.71%; HSS, 90.44%) attested to the efficacy and resilience of the suggested model, underscoring its ability to generalize and produce accurate predictions under a variety of circumstances. In conclusion, the combination of Xception and VGG16 into a new meta-architecture added the strengths of both models, and self-supervised learning helped learn meaningful representations without the need for explicit labels. |
| format | Article |
| id | doaj-art-9502613e63d5415b9b4acd5e2beaac8f |
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| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9502613e63d5415b9b4acd5e2beaac8f2025-08-20T02:00:13ZengIEEEIEEE Access2169-35362024-01-011219282819283910.1109/ACCESS.2024.351145610776944Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-ArchitectureSaul Huaquipaco0https://orcid.org/0000-0003-2323-3061Oscar Vera1https://orcid.org/0000-0002-1996-8471Victor Yana-Mamani2https://orcid.org/0000-0003-0982-2353Wilson Mamani3https://orcid.org/0000-0002-5408-4284Helarf Calsina4https://orcid.org/0000-0002-8565-805XFlavio Puma5https://orcid.org/0009-0001-6451-8381Eli Morales-Rojas6https://orcid.org/0000-0002-8623-3192Norman Beltran7https://orcid.org/0000-0002-1597-2991Jose Cruz8https://orcid.org/0000-0002-5201-0265School of Ingeniería de Sistemas e Informática, Faculty of Engineering, Universidad Nacional de Moquegua, Moquegua, PeruSchool of Ingeniería de Sistemas e Informática, Faculty of Engineering, Universidad Nacional de Moquegua, Moquegua, PeruSchool of Ingeniería de Sistemas e Informática, Faculty of Engineering, Universidad Nacional de Moquegua, Moquegua, PeruUniversidad de Alicante, Alicante, SpainSchool of Ingeniería Mecatrónica, Faculty of Engineering, Universidad Nacional de Juliaca, Juliaca, PeruPostgraduate Unit of EPG-UNA-PUNO, Universidad Nacional del Altiplano de Puno, Puno, PeruFaculty of Ingeniería de Sistemas y Mecánica Eléctrica, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Chachapoyas, PeruSchool of Ingeniería Electrónica, Faculty of FIMEES, Universidad Nacional del Altiplano de Puno, Puno, PeruSchool of Ingeniería Electrónica, Faculty of FIMEES, Universidad Nacional del Altiplano de Puno, Puno, PeruSpilocaea oleagina is a common and dangerous fungal disease in olive trees that significantly reduces olive production. The early and accurate detection of this disease is essential for implementing effective control measures. In this study, we propose the creation of a new meta-architecture for identifying peacock spots on olive leaves. This new meta-architecture integrates Xception and VGG16 as the basis for the methodology employed in this study. Furthermore, a machine learning approach was used to pretrain the model in an unsupervised manner, thereby improving its generalization capacity. Metrics such as Kappa (K), True Skill Statistic (TSS), Proportion of Expected Success (Pe), Threat Index (Ts), and the Heidke Skill Score (HSS) were used to validate the model results. Based on these indicators, we evaluated the robustness, accuracy, and ability of the model to identify peacock spots. The testing results showed that the suggested meta-architecture, named SSL-XceVNet, substantially outperformed the baseline XceVNet model (88.24%) in detecting peacock spots, with an accuracy of 95.22%. The validation measurement results (K, 73.57%; TSS, 72.66%; Pe, 55.06%; Ts, 90.71%; HSS, 90.44%) attested to the efficacy and resilience of the suggested model, underscoring its ability to generalize and produce accurate predictions under a variety of circumstances. In conclusion, the combination of Xception and VGG16 into a new meta-architecture added the strengths of both models, and self-supervised learning helped learn meaningful representations without the need for explicit labels.https://ieeexplore.ieee.org/document/10776944/Spilocaea oleaginaself-supervised learningVGG16XceptionXceVNet |
| spellingShingle | Saul Huaquipaco Oscar Vera Victor Yana-Mamani Wilson Mamani Helarf Calsina Flavio Puma Eli Morales-Rojas Norman Beltran Jose Cruz Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture IEEE Access Spilocaea oleagina self-supervised learning VGG16 Xception XceVNet |
| title | Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture |
| title_full | Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture |
| title_fullStr | Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture |
| title_full_unstemmed | Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture |
| title_short | Peacock Spot Detection in Olive Leaves Using Self Supervised Learning in an Assembly Meta-Architecture |
| title_sort | peacock spot detection in olive leaves using self supervised learning in an assembly meta architecture |
| topic | Spilocaea oleagina self-supervised learning VGG16 Xception XceVNet |
| url | https://ieeexplore.ieee.org/document/10776944/ |
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