Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
Most of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent wo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4565 |
| _version_ | 1797608311462297600 |
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| author | Adrian F. Chavarro Diego Renza Dora M. Ballesteros |
| author_facet | Adrian F. Chavarro Diego Renza Dora M. Ballesteros |
| author_sort | Adrian F. Chavarro |
| collection | DOAJ |
| description | Most of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops. |
| first_indexed | 2024-03-11T05:41:43Z |
| format | Article |
| id | doaj.art-38b1d83051e34ecea5704aafbdbb1de6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:41:43Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-38b1d83051e34ecea5704aafbdbb1de62023-11-17T16:22:10ZengMDPI AGApplied Sciences2076-34172023-04-01137456510.3390/app13074565Influence of Hyperparameters in Deep Learning Models for Coffee Rust DetectionAdrian F. Chavarro0Diego Renza1Dora M. Ballesteros2Doctorado en Ciencias Aplicadas, Universidad Militar Nueva Granada, Cajica 250247, ColombiaDoctorado en Ciencias Aplicadas, Universidad Militar Nueva Granada, Cajica 250247, ColombiaDoctorado en Ciencias Aplicadas, Universidad Militar Nueva Granada, Cajica 250247, ColombiaMost of the world’s crops can be attacked by various diseases or pests, affecting their quality and productivity. In recent years, transfer learning with deep learning (DL) models has been used to detect diseases in maize, tomato, rice, and other crops. In the specific case of coffee, some recent works have used fixed hyperparameters to fine-tune the pre-trained models with the new dataset and/or applied data augmentation, such as image patching, to improve classifier performance. However, a detailed evaluation of the impact of architecture (e.g., backbone) and training (e.g., optimizer and learning rate) hyperparameters on the performance of coffee rust classification models has not been performed. Therefore, this paper presents a comprehensive study of the impact of five types of hyperparameters on the performance of coffee rust classification models. Specifically, eight pre-trained models are compared, each with four different amounts of transferred layers and three different numbers of neurons in the fully-connected (FC) layer, and the models are fine-tuned with three types of optimizers, each with three learning rate values. Comparing more than 800 models in terms of F1-score and accuracy, it is identified that the type of backbone is the hyperparameter with the greatest impact (with differences between models of up to 70%), followed by the optimizer (with differences of up to 20%). At the end of the study, specific recommendations are made on the values of the most suitable hyperparameters for the identification of this type of disease in coffee crops.https://www.mdpi.com/2076-3417/13/7/4565deep learningCNNcoffee rusttransfer learningDenseNetXception |
| spellingShingle | Adrian F. Chavarro Diego Renza Dora M. Ballesteros Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection Applied Sciences deep learning CNN coffee rust transfer learning DenseNet Xception |
| title | Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection |
| title_full | Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection |
| title_fullStr | Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection |
| title_full_unstemmed | Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection |
| title_short | Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection |
| title_sort | influence of hyperparameters in deep learning models for coffee rust detection |
| topic | deep learning CNN coffee rust transfer learning DenseNet Xception |
| url | https://www.mdpi.com/2076-3417/13/7/4565 |
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