Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning
Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases....
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| Format: | Article |
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MDPI AG
2021-01-01
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| Series: | Pathogens |
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| Online Access: | https://www.mdpi.com/2076-0817/10/2/131 |
| _version_ | 1797406573905051648 |
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| author | Malusi Sibiya Mbuyu Sumbwanyambe |
| author_facet | Malusi Sibiya Mbuyu Sumbwanyambe |
| author_sort | Malusi Sibiya |
| collection | DOAJ |
| description | Many applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage. |
| first_indexed | 2024-03-09T03:28:23Z |
| format | Article |
| id | doaj.art-63255f82b7e04cefa45bae7e24ea455a |
| institution | Directory Open Access Journal |
| issn | 2076-0817 |
| language | English |
| last_indexed | 2024-03-09T03:28:23Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pathogens |
| spelling | doaj.art-63255f82b7e04cefa45bae7e24ea455a2023-12-03T14:58:44ZengMDPI AGPathogens2076-08172021-01-0110213110.3390/pathogens10020131Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep LearningMalusi Sibiya0Mbuyu Sumbwanyambe1Department of Electrical and Mining Engineering, University of South Africa, 28 Pioneer Ave, Florida Park, Johannesburg, Roodepoort 1709, South AfricaDepartment of Electrical and Mining Engineering, University of South Africa, 28 Pioneer Ave, Florida Park, Johannesburg, Roodepoort 1709, South AfricaMany applications of plant pathology had been enabled by the evolution of artificial intelligence (AI). For instance, many researchers had used pre-trained convolutional neural networks (CNNs) such as the VGG-16, Inception, and Google Net to mention a few, for the classifications of plant diseases. The trend of using AI for plant disease classification has grown to such an extent that some researchers were able to use artificial intelligence to also detect their severities. The purpose of this study is to introduce a novel approach that is reliable in predicting severities of the maize common rust disease by CNN deep learning models. This was achieved by applying threshold-segmentation on images of diseased maize leaves (Common Rust disease) to extract the percentage of the diseased leaf area which was then used to derive fuzzy decision rules for the assignment of Common Rust images to their severity classes. The four severity classes were then used to train a VGG-16 network in order to automatically classify the test images of the Common Rust disease according to their classes of severity. Trained with images developed by using this proposed approach, the VGG-16 network achieved a validation accuracy of 95.63% and a testing accuracy of 89% when tested on images of the Common Rust disease among four classes of disease severity named Early stage, Middle stage, Late Stage and Healthy stage.https://www.mdpi.com/2076-0817/10/2/131VGG-16common rustconvolutional neural networksimage histogramsfuzzy decision rulesOtsu threshold method |
| spellingShingle | Malusi Sibiya Mbuyu Sumbwanyambe Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning Pathogens VGG-16 common rust convolutional neural networks image histograms fuzzy decision rules Otsu threshold method |
| title | Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning |
| title_full | Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning |
| title_fullStr | Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning |
| title_full_unstemmed | Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning |
| title_short | Automatic Fuzzy Logic-Based Maize Common Rust Disease Severity Predictions with Thresholding and Deep Learning |
| title_sort | automatic fuzzy logic based maize common rust disease severity predictions with thresholding and deep learning |
| topic | VGG-16 common rust convolutional neural networks image histograms fuzzy decision rules Otsu threshold method |
| url | https://www.mdpi.com/2076-0817/10/2/131 |
| work_keys_str_mv | AT malusisibiya automaticfuzzylogicbasedmaizecommonrustdiseaseseveritypredictionswiththresholdinganddeeplearning AT mbuyusumbwanyambe automaticfuzzylogicbasedmaizecommonrustdiseaseseveritypredictionswiththresholdinganddeeplearning |