Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture
This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9627678/ |
| _version_ | 1818388246511484928 |
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| author | Stefania Barburiceanu Serban Meza Bogdan Orza Raul Malutan Romulus Terebes |
| author_facet | Stefania Barburiceanu Serban Meza Bogdan Orza Raul Malutan Romulus Terebes |
| author_sort | Stefania Barburiceanu |
| collection | DOAJ |
| description | This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impact on agricultural productivity and quality. Within this context, we propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases. We focus on results relevant to real-time processing scenarios that can be easily transferred to manned/unmanned agricultural smart machinery (e.g. tractors, drones, robots, IoT smart sensor networks, etc.) by reconsidering the common processing pipeline. In our approach, texture features are extracted from different layers of pre-trained Convolutional Neural Network models and are later applied to a machine-learning classifier. For the experimental evaluation, we used publicly available datasets consisting of RGB textured images and datasets containing images of healthy and non-healthy plant leaves of different species. We compared our method to feature vectors derived from traditional handcrafted feature extraction descriptors computed for the same images and end-to-end deep-learning approaches. The proposed method proves to be significantly more efficient in terms of processing times and discriminative power, being able to surpass traditional and end-to-end CNN-based methods and provide a solution also to the problem of the reduced datasets available for precision agriculture. |
| first_indexed | 2024-12-14T04:22:47Z |
| format | Article |
| id | doaj.art-0632f652f7234c44b02637ffa43d589b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T04:22:47Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0632f652f7234c44b02637ffa43d589b2022-12-21T23:17:17ZengIEEEIEEE Access2169-35362021-01-01916008516010310.1109/ACCESS.2021.31310029627678Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision AgricultureStefania Barburiceanu0https://orcid.org/0000-0002-9988-5966Serban Meza1https://orcid.org/0000-0002-9109-0659Bogdan Orza2https://orcid.org/0000-0002-0544-903XRaul Malutan3Romulus Terebes4Communications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaCommunications Department, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaThis paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks. Research related to precision agriculture is of high relevance due to its potential economic impact on agricultural productivity and quality. Within this context, we propose a deep learning-based feature extraction method for the identification of plant species and the classification of plant leaf diseases. We focus on results relevant to real-time processing scenarios that can be easily transferred to manned/unmanned agricultural smart machinery (e.g. tractors, drones, robots, IoT smart sensor networks, etc.) by reconsidering the common processing pipeline. In our approach, texture features are extracted from different layers of pre-trained Convolutional Neural Network models and are later applied to a machine-learning classifier. For the experimental evaluation, we used publicly available datasets consisting of RGB textured images and datasets containing images of healthy and non-healthy plant leaves of different species. We compared our method to feature vectors derived from traditional handcrafted feature extraction descriptors computed for the same images and end-to-end deep-learning approaches. The proposed method proves to be significantly more efficient in terms of processing times and discriminative power, being able to surpass traditional and end-to-end CNN-based methods and provide a solution also to the problem of the reduced datasets available for precision agriculture.https://ieeexplore.ieee.org/document/9627678/Applied convolutional neural networksleaf disease detectionimage classificationtexture classificationtexture feature extraction |
| spellingShingle | Stefania Barburiceanu Serban Meza Bogdan Orza Raul Malutan Romulus Terebes Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture IEEE Access Applied convolutional neural networks leaf disease detection image classification texture classification texture feature extraction |
| title | Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture |
| title_full | Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture |
| title_fullStr | Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture |
| title_full_unstemmed | Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture |
| title_short | Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture |
| title_sort | convolutional neural networks for texture feature extraction applications to leaf disease classification in precision agriculture |
| topic | Applied convolutional neural networks leaf disease detection image classification texture classification texture feature extraction |
| url | https://ieeexplore.ieee.org/document/9627678/ |
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