Using transfer learning-based plant disease classification and detection for sustainable agriculture
Abstract Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detectio...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
|
| Series: | BMC Plant Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12870-024-04825-y |
| _version_ | 1797275283813826560 |
|---|---|
| author | Wasswa Shafik Ali Tufail Chandratilak De Silva Liyanage Rosyzie Anna Awg Haji Mohd Apong |
| author_facet | Wasswa Shafik Ali Tufail Chandratilak De Silva Liyanage Rosyzie Anna Awg Haji Mohd Apong |
| author_sort | Wasswa Shafik |
| collection | DOAJ |
| description | Abstract Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification. |
| first_indexed | 2024-03-07T15:11:05Z |
| format | Article |
| id | doaj.art-7099c0506b024d119154d0ac594de6f6 |
| institution | Directory Open Access Journal |
| issn | 1471-2229 |
| language | English |
| last_indexed | 2024-03-07T15:11:05Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Plant Biology |
| spelling | doaj.art-7099c0506b024d119154d0ac594de6f62024-03-05T18:38:20ZengBMCBMC Plant Biology1471-22292024-02-0124111910.1186/s12870-024-04825-yUsing transfer learning-based plant disease classification and detection for sustainable agricultureWasswa Shafik0Ali Tufail1Chandratilak De Silva Liyanage2Rosyzie Anna Awg Haji Mohd Apong3School of Digital Science, Universiti Brunei DarussalamSchool of Digital Science, Universiti Brunei DarussalamSchool of Digital Science, Universiti Brunei DarussalamSchool of Digital Science, Universiti Brunei DarussalamAbstract Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's “Zero Hunger,” “Climate Action,” and “Responsible Consumption and Production” sustainable development goals. In addition to already available methods for early disease detection and classification facing overfitting and fine feature extraction complexities during the training process, how early signs of green attacks can be identified or classified remains uncertain. Most pests and disease symptoms are seen in plant leaves and fruits, yet their diagnosis by experts in the laboratory is expensive, tedious, labor-intensive, and time-consuming. Notably, how plant pests and diseases can be appropriately detected and timely prevented is a hotspot paradigm in smart, sustainable agriculture remains unknown. In recent years, deep transfer learning has demonstrated tremendous advances in the recognition accuracy of object detection and image classification systems since these frameworks utilize previously acquired knowledge to solve similar problems more effectively and quickly. Therefore, in this research, we introduce two plant disease detection (PDDNet) models of early fusion (AE) and the lead voting ensemble (LVE) integrated with nine pre-trained convolutional neural networks (CNNs) and fine-tuned by deep feature extraction for efficient plant disease identification and classification. The experiments were carried out on 15 classes of the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 categories. Hyperparameter fine-tuning was done with popular pre-trained models, including DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, ResNet18, EfficientNetB7, NASNetMobile, and ConvNeXtSmall. We test these CNNs on the stated plant disease detection and classification problem, both independently and as part of an ensemble. In the final phase, a logistic regression (LR) classifier is utilized to determine the performance of various CNN model combinations. A comparative analysis was also performed on classifiers, deep learning, the proposed model, and similar state-of-the-art studies. The experiments demonstrated that PDDNet-AE and PDDNet-LVE achieved 96.74% and 97.79%, respectively, compared to current CNNs when tested on several plant diseases, depicting its exceptional robustness and generalization capabilities and mitigating current concerns in plant disease detection and classification.https://doi.org/10.1186/s12870-024-04825-yClimate actionConvolutional neural networksFeature extractionLogistic regressionPlant diseasesResponsible consumption and production |
| spellingShingle | Wasswa Shafik Ali Tufail Chandratilak De Silva Liyanage Rosyzie Anna Awg Haji Mohd Apong Using transfer learning-based plant disease classification and detection for sustainable agriculture BMC Plant Biology Climate action Convolutional neural networks Feature extraction Logistic regression Plant diseases Responsible consumption and production |
| title | Using transfer learning-based plant disease classification and detection for sustainable agriculture |
| title_full | Using transfer learning-based plant disease classification and detection for sustainable agriculture |
| title_fullStr | Using transfer learning-based plant disease classification and detection for sustainable agriculture |
| title_full_unstemmed | Using transfer learning-based plant disease classification and detection for sustainable agriculture |
| title_short | Using transfer learning-based plant disease classification and detection for sustainable agriculture |
| title_sort | using transfer learning based plant disease classification and detection for sustainable agriculture |
| topic | Climate action Convolutional neural networks Feature extraction Logistic regression Plant diseases Responsible consumption and production |
| url | https://doi.org/10.1186/s12870-024-04825-y |
| work_keys_str_mv | AT wasswashafik usingtransferlearningbasedplantdiseaseclassificationanddetectionforsustainableagriculture AT alitufail usingtransferlearningbasedplantdiseaseclassificationanddetectionforsustainableagriculture AT chandratilakdesilvaliyanage usingtransferlearningbasedplantdiseaseclassificationanddetectionforsustainableagriculture AT rosyzieannaawghajimohdapong usingtransferlearningbasedplantdiseaseclassificationanddetectionforsustainableagriculture |