Automatic Detection of Tomato Diseases Using Deep Transfer Learning
Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing econo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8467 |
| _version_ | 1827667214811004928 |
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| author | Natheer Khasawneh Esraa Faouri Mohammad Fraiwan |
| author_facet | Natheer Khasawneh Esraa Faouri Mohammad Fraiwan |
| author_sort | Natheer Khasawneh |
| collection | DOAJ |
| description | Global food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control. |
| first_indexed | 2024-03-10T03:04:39Z |
| format | Article |
| id | doaj.art-5e9581e424de4ca9b894ebde33c8be15 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:04:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5e9581e424de4ca9b894ebde33c8be152023-11-23T12:40:03ZengMDPI AGApplied Sciences2076-34172022-08-011217846710.3390/app12178467Automatic Detection of Tomato Diseases Using Deep Transfer LearningNatheer Khasawneh0Esraa Faouri1Mohammad Fraiwan2Department of Software Engineering, Jordan University of Science and Technology, Irbid 22110, JordanDepartment of Computer Engineering, Jordan University of Science and Technology, Irbid 22110, JordanDepartment of Computer Engineering, Jordan University of Science and Technology, Irbid 22110, JordanGlobal food production is being strained by extreme weather conditions, fluctuating temperatures, and geopolitics. Tomato is a staple agricultural product with tens of millions of tons produced every year worldwide. Thus, preserving the tomato plant from diseases will go a long way in reducing economical loss and boost output. Technological innovations have great potential in facilitating disease detection and control. More specifically, artificial intelligence algorithms in the form of deep learning methods have established themselves in many real-life applications in a wide range of disciplines (e.g., medicine, agriculture, or facial recognition, etc.). In this paper, we aim at applying deep transfer learning in the classification of nine tomato diseases (i.e., bacterial spot, early blight, late blight, leaf mold, mosaic virus, septoria leaf spot, spider mites, target spot, and yellow leaf curl virus) in addition to the healthy state. The approach in this work uses leaf images as input, which is fed to convolutional neural network models. No preprocessing, feature extraction, or image processing is required. Moreover, the models are based on transfer learning of well-established deep learning networks. The performance was extensively evaluated using multiple strategies for data split and a number of metrics. In addition, the experiments were repeated 10 times to account for randomness. The ten categories were classified with mean values of 99.3% precision, 99.2% F1 score, 99.1% recall, and 99.4% accuracy. Such results show that it is highly feasible to develop smartphone-based applications that can aid plant pathologists and farmers to quickly and accurately perform disease detection and subsequent control.https://www.mdpi.com/2076-3417/12/17/8467deep learningtomatoesvirusbacteriablightspot |
| spellingShingle | Natheer Khasawneh Esraa Faouri Mohammad Fraiwan Automatic Detection of Tomato Diseases Using Deep Transfer Learning Applied Sciences deep learning tomatoes virus bacteria blight spot |
| title | Automatic Detection of Tomato Diseases Using Deep Transfer Learning |
| title_full | Automatic Detection of Tomato Diseases Using Deep Transfer Learning |
| title_fullStr | Automatic Detection of Tomato Diseases Using Deep Transfer Learning |
| title_full_unstemmed | Automatic Detection of Tomato Diseases Using Deep Transfer Learning |
| title_short | Automatic Detection of Tomato Diseases Using Deep Transfer Learning |
| title_sort | automatic detection of tomato diseases using deep transfer learning |
| topic | deep learning tomatoes virus bacteria blight spot |
| url | https://www.mdpi.com/2076-3417/12/17/8467 |
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