Ensemble of CNN models for classification of groundnut plant leaf disease detection
Plant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehens...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523001909 |
| _version_ | 1797390383392489472 |
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| author | Aishwarya M．P． Padmanabha Reddy |
| author_facet | Aishwarya M．P． Padmanabha Reddy |
| author_sort | Aishwarya M．P． |
| collection | DOAJ |
| description | Plant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehensive dataset of groundnut leaf images was created in collaboration with a pathologist, facilitating the automated identification of plant diseases. The field of image classification has found CNN to be quite effective. In this study, we create such a method for disease identification and classification by utilizing a tri-CNN architecture consisting of DenseNet169, Inception, and Xception —that have been pre-trained on the ImageNet dataset using two created non-linear equations on decision scores from the before mentioned base learners,the outlined ensemble methodology generates ultimate predictions for the test samples by incorporating the scores of four conventional metrics for evaluation, namely recall, precision, accuracy, and f1-score, obtained from the base learners. Hence mentioned CNN customized models are used to train our model for obtaining better results. The proposed approach evaluated on real world groundnut leaf dataset. The model that was put forth attained accuracy rates of 98.46 %. |
| first_indexed | 2024-03-08T23:09:57Z |
| format | Article |
| id | doaj.art-e8b75755ba67426b860550b89114572a |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-03-08T23:09:57Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-e8b75755ba67426b860550b89114572a2023-12-15T07:27:16ZengElsevierSmart Agricultural Technology2772-37552023-12-016100362Ensemble of CNN models for classification of groundnut plant leaf disease detectionAishwarya M．P．0Padmanabha Reddy1Corresponding author.; Vijayanagara Sri Krishnadevaraya University, Bellary, IndiaVijayanagara Sri Krishnadevaraya University, Bellary, IndiaPlant diseases pose a significant threat to the world's nutrition and can have severe consequences for smallholder farmers who rely on a thriving groundnut crop for their livelihoods. Therefore, it is crucial to create an algorithm for early automated diagnosis of plant diseases. Soa comprehensive dataset of groundnut leaf images was created in collaboration with a pathologist, facilitating the automated identification of plant diseases. The field of image classification has found CNN to be quite effective. In this study, we create such a method for disease identification and classification by utilizing a tri-CNN architecture consisting of DenseNet169, Inception, and Xception —that have been pre-trained on the ImageNet dataset using two created non-linear equations on decision scores from the before mentioned base learners,the outlined ensemble methodology generates ultimate predictions for the test samples by incorporating the scores of four conventional metrics for evaluation, namely recall, precision, accuracy, and f1-score, obtained from the base learners. Hence mentioned CNN customized models are used to train our model for obtaining better results. The proposed approach evaluated on real world groundnut leaf dataset. The model that was put forth attained accuracy rates of 98.46 %.http://www.sciencedirect.com/science/article/pii/S2772375523001909Deep learningDensenet-169InceptionV3Xception |
| spellingShingle | Aishwarya M．P． Padmanabha Reddy Ensemble of CNN models for classification of groundnut plant leaf disease detection Smart Agricultural Technology Deep learning Densenet-169 InceptionV3 Xception |
| title | Ensemble of CNN models for classification of groundnut plant leaf disease detection |
| title_full | Ensemble of CNN models for classification of groundnut plant leaf disease detection |
| title_fullStr | Ensemble of CNN models for classification of groundnut plant leaf disease detection |
| title_full_unstemmed | Ensemble of CNN models for classification of groundnut plant leaf disease detection |
| title_short | Ensemble of CNN models for classification of groundnut plant leaf disease detection |
| title_sort | ensemble of cnn models for classification of groundnut plant leaf disease detection |
| topic | Deep learning Densenet-169 InceptionV3 Xception |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523001909 |
| work_keys_str_mv | AT aishwaryamp ensembleofcnnmodelsforclassificationofgroundnutplantleafdiseasedetection AT padmanabhareddy ensembleofcnnmodelsforclassificationofgroundnutplantleafdiseasedetection |