Using Multioutput Learning to Diagnose Plant Disease and Stress Severity
Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient ne...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6663442 |
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| author | Gianni Fenu Francesca Maridina Malloci |
| author_facet | Gianni Fenu Francesca Maridina Malloci |
| author_sort | Gianni Fenu |
| collection | DOAJ |
| description | Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm. |
| format | Article |
| id | doaj-art-e859c08a47cf43a79656148def45d0da |
| institution | DOAJ |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e859c08a47cf43a79656148def45d0da2025-08-20T03:21:03ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66634426663442Using Multioutput Learning to Diagnose Plant Disease and Stress SeverityGianni Fenu0Francesca Maridina Malloci1Department of Mathematics and Computer Science, University of Cagliari, Cagliari 09124, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, Cagliari 09124, ItalyEarly diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.http://dx.doi.org/10.1155/2021/6663442 |
| spellingShingle | Gianni Fenu Francesca Maridina Malloci Using Multioutput Learning to Diagnose Plant Disease and Stress Severity Complexity |
| title | Using Multioutput Learning to Diagnose Plant Disease and Stress Severity |
| title_full | Using Multioutput Learning to Diagnose Plant Disease and Stress Severity |
| title_fullStr | Using Multioutput Learning to Diagnose Plant Disease and Stress Severity |
| title_full_unstemmed | Using Multioutput Learning to Diagnose Plant Disease and Stress Severity |
| title_short | Using Multioutput Learning to Diagnose Plant Disease and Stress Severity |
| title_sort | using multioutput learning to diagnose plant disease and stress severity |
| url | http://dx.doi.org/10.1155/2021/6663442 |
| work_keys_str_mv | AT giannifenu usingmultioutputlearningtodiagnoseplantdiseaseandstressseverity AT francescamaridinamalloci usingmultioutputlearningtodiagnoseplantdiseaseandstressseverity |