One-shot learning with triplet loss for vegetation classification tasks
Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification. In our research, we focused on two tasks related to veg...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2021-07-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | http://computeroptics.ru/eng/KO/Annot/KO45-4/450416e.html |
| _version_ | 1818356659957792768 |
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| author | A.V. Uzhinskiy G.A. Ososkov P.V. Goncharov A.V. Nechaevskiy A.A. Smetanin |
| author_facet | A.V. Uzhinskiy G.A. Ososkov P.V. Goncharov A.V. Nechaevskiy A.A. Smetanin |
| author_sort | A.V. Uzhinskiy |
| collection | DOAJ |
| description | Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification. In our research, we focused on two tasks related to vegetation. The first one is plant disease detection on 25 classes of five crops (grape, cotton, wheat, cucumbers, and corn). This task is motivated because harvest losses due to diseases is a serious problem for both large farming structures and rural families. The second task is the identification of moss species (5 classes). Mosses are natural bioaccumulators of pollutants; therefore, they are used in environmental monitoring programs. The identification of moss species is an important step in the sample preprocessing. In both tasks, we used self-collected image databases. We tried several deep learning architectures and approaches. Our Siamese network architecture with a triplet loss function and MobileNetV2 as a base network showed the most impressive results in both above-mentioned tasks. The average accuracy for plant disease detection amounted to over 97.8% and 97.6% for moss species classification. |
| first_indexed | 2024-12-13T20:00:44Z |
| format | Article |
| id | doaj.art-c7ece2517d11406d90dcdab84b5e953d |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-12-13T20:00:44Z |
| publishDate | 2021-07-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-c7ece2517d11406d90dcdab84b5e953d2022-12-21T23:33:11ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792021-07-0145460861410.18287/2412-6179-CO-856One-shot learning with triplet loss for vegetation classification tasksA.V. Uzhinskiy0G.A. Ososkov1P.V. Goncharov2A.V. Nechaevskiy3A.A. Smetanin4Joint Institute for Nuclear Research, 141980, Russia, Dubna, Joliot-Curie 6; Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, Russia, Moscow, Timiryazevskaya st., 49Joint Institute for Nuclear Research, 141980, Russia, Dubna, Joliot-Curie 6Joint Institute for Nuclear Research, 141980, Russia, Dubna, Joliot-Curie 6Joint Institute for Nuclear Research, 141980, Russia, Dubna, Joliot-Curie 6; Russian State Agrarian University - Moscow Timiryazev Agricultural Academy, Russia, Moscow, Timiryazevskaya st., 49National Research University ITMO, 197101, Russia, Saint-Petersburg, Kronverkskiy pr., 49Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and this kind of loss for face recognition and object classification. In our research, we focused on two tasks related to vegetation. The first one is plant disease detection on 25 classes of five crops (grape, cotton, wheat, cucumbers, and corn). This task is motivated because harvest losses due to diseases is a serious problem for both large farming structures and rural families. The second task is the identification of moss species (5 classes). Mosses are natural bioaccumulators of pollutants; therefore, they are used in environmental monitoring programs. The identification of moss species is an important step in the sample preprocessing. In both tasks, we used self-collected image databases. We tried several deep learning architectures and approaches. Our Siamese network architecture with a triplet loss function and MobileNetV2 as a base network showed the most impressive results in both above-mentioned tasks. The average accuracy for plant disease detection amounted to over 97.8% and 97.6% for moss species classification.http://computeroptics.ru/eng/KO/Annot/KO45-4/450416e.htmldeep neural networkssiamese networkstriplet lossplant diseases detectionmoss species classification |
| spellingShingle | A.V. Uzhinskiy G.A. Ososkov P.V. Goncharov A.V. Nechaevskiy A.A. Smetanin One-shot learning with triplet loss for vegetation classification tasks Компьютерная оптика deep neural networks siamese networks triplet loss plant diseases detection moss species classification |
| title | One-shot learning with triplet loss for vegetation classification tasks |
| title_full | One-shot learning with triplet loss for vegetation classification tasks |
| title_fullStr | One-shot learning with triplet loss for vegetation classification tasks |
| title_full_unstemmed | One-shot learning with triplet loss for vegetation classification tasks |
| title_short | One-shot learning with triplet loss for vegetation classification tasks |
| title_sort | one shot learning with triplet loss for vegetation classification tasks |
| topic | deep neural networks siamese networks triplet loss plant diseases detection moss species classification |
| url | http://computeroptics.ru/eng/KO/Annot/KO45-4/450416e.html |
| work_keys_str_mv | AT avuzhinskiy oneshotlearningwithtripletlossforvegetationclassificationtasks AT gaososkov oneshotlearningwithtripletlossforvegetationclassificationtasks AT pvgoncharov oneshotlearningwithtripletlossforvegetationclassificationtasks AT avnechaevskiy oneshotlearningwithtripletlossforvegetationclassificationtasks AT aasmetanin oneshotlearningwithtripletlossforvegetationclassificationtasks |