IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples
Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attenti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4348 |
| _version_ | 1797608347091861504 |
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| author | Ruofan Zhang Yi Wang Ping Jiang Jialiang Peng Hailin Chen |
| author_facet | Ruofan Zhang Yi Wang Ping Jiang Jialiang Peng Hailin Chen |
| author_sort | Ruofan Zhang |
| collection | DOAJ |
| description | Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attention mechanism to enhance feature representation. The model is optimized by employing the IBMax module to increase the receptive field and adding the HardSwish function to the ConvBN layer to improve stability and speed. To address the challenge of poor generalization of models trained on public datasets to real environment datasets, we developed an improved PlantDoc++ dataset and utilized transfer learning to pre-train the model on PDDA and PlantVillage datasets. The results indicate that after pre-training on the PDDA dataset, IBSA_Net achieved a test accuracy of 0.946 on a real environment dataset, with an average precision, recall, and F1-score of 0.942, 0.944, and 0.943, respectively. Additionally, the effectiveness of IBSA_Net in other crops is verified. This study provides a dependable and effective method for recognizing tomato leaf diseases in real agricultural production environments, with the potential for application in other crops. |
| first_indexed | 2024-03-11T05:43:10Z |
| format | Article |
| id | doaj.art-b6840035d650426b959ee5856e1fbf31 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:43:10Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b6840035d650426b959ee5856e1fbf312023-11-17T16:19:04ZengMDPI AGApplied Sciences2076-34172023-03-01137434810.3390/app13074348IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small SamplesRuofan Zhang0Yi Wang1Ping Jiang2Jialiang Peng3Hailin Chen4College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha 410128, ChinaTomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attention mechanism to enhance feature representation. The model is optimized by employing the IBMax module to increase the receptive field and adding the HardSwish function to the ConvBN layer to improve stability and speed. To address the challenge of poor generalization of models trained on public datasets to real environment datasets, we developed an improved PlantDoc++ dataset and utilized transfer learning to pre-train the model on PDDA and PlantVillage datasets. The results indicate that after pre-training on the PDDA dataset, IBSA_Net achieved a test accuracy of 0.946 on a real environment dataset, with an average precision, recall, and F1-score of 0.942, 0.944, and 0.943, respectively. Additionally, the effectiveness of IBSA_Net in other crops is verified. This study provides a dependable and effective method for recognizing tomato leaf diseases in real agricultural production environments, with the potential for application in other crops.https://www.mdpi.com/2076-3417/13/7/4348tomatotransfer learningsmall sampleIBSA_Netdisease recognition |
| spellingShingle | Ruofan Zhang Yi Wang Ping Jiang Jialiang Peng Hailin Chen IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples Applied Sciences tomato transfer learning small sample IBSA_Net disease recognition |
| title | IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples |
| title_full | IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples |
| title_fullStr | IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples |
| title_full_unstemmed | IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples |
| title_short | IBSA_Net: A Network for Tomato Leaf Disease Identification Based on Transfer Learning with Small Samples |
| title_sort | ibsa net a network for tomato leaf disease identification based on transfer learning with small samples |
| topic | tomato transfer learning small sample IBSA_Net disease recognition |
| url | https://www.mdpi.com/2076-3417/13/7/4348 |
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