Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection
In agriculture, accurate and timely detection of plant diseases is crucial for minimizing crop losses and ensuring food security. Traditional methods of plant disease detection often rely on visual inspection and single-modal data analysis, which can be limited in their diagnostic accuracy. This stu...
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2024-01-01
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| author | Anita Shrotriya Akhilesh Kumar Sharma Amit Kumar Bairwa R. Manoj |
| author_facet | Anita Shrotriya Akhilesh Kumar Sharma Amit Kumar Bairwa R. Manoj |
| author_sort | Anita Shrotriya |
| collection | DOAJ |
| description | In agriculture, accurate and timely detection of plant diseases is crucial for minimizing crop losses and ensuring food security. Traditional methods of plant disease detection often rely on visual inspection and single-modal data analysis, which can be limited in their diagnostic accuracy. This study introduces an innovative ensemble learning framework that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for multimodal plant disease detection to address these limitations. The proposed framework capitalizes on the strengths of both CNNs and RNNs by processing visual data from leaf images and sequential data such as time-series measurements of environmental conditions. CNNs are adept at extracting intricate spatial features from leaf images, identifying visual symptoms of diseases with high precision. Concurrently, RNNs are designed to capture temporal patterns in sequential data, providing insights into environmental factors that may influence disease development. The ensemble method employed in this study aggregates predictions from both CNN and RNN models using techniques such as majority voting and weighted averaging. Majority voting involves combining the outputs of multiple models to make a final prediction based on the most common outcome, while weighted averaging assigns different weights to the predictions of each model based on their performance, leading to a more balanced and accurate diagnostic result. Experimental evaluations were conducted on comprehensive multimodal datasets, including diverse plant species and varying environmental conditions, to assess the effectiveness of the proposed framework. The results demonstrate that the ensemble approach significantly outperforms individual CNN and RNN models, achieving higher diagnostic accuracy, precision, recall, and F1 scores. This superior performance underscores the potential of integrating diverse data streams to provide a holistic view of plant health, enabling more accurate and reliable disease diagnosis. The findings of this study highlight the importance of leveraging multimodal data and advanced machine-learning techniques in plant disease detection. By integrating spatial and temporal information, the proposed framework offers a comprehensive diagnostic tool that can be instrumental in improving agricultural practices, optimizing plant health management, and ultimately contributing to sustainable farming practices. |
| format | Article |
| id | doaj-art-c37a76bbe73f4521a82adbcd0ebc8d0b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c37a76bbe73f4521a82adbcd0ebc8d0b2025-01-01T00:01:37ZengIEEEIEEE Access2169-35362024-01-011219802819804510.1109/ACCESS.2024.351584310792887Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease DetectionAnita Shrotriya0https://orcid.org/0000-0002-5520-9448Akhilesh Kumar Sharma1https://orcid.org/0000-0002-7308-7800Amit Kumar Bairwa2https://orcid.org/0000-0003-1830-0661R. Manoj3https://orcid.org/0000-0002-6420-4155Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, IndiaDepartment of Data Science and Engineering, Manipal University Jaipur, Jaipur, IndiaDepartment of Artificial Intelligence and Machine Learning, Manipal University Jaipur, Jaipur, IndiaDepartment of Computer Science and Engineering, Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, IndiaIn agriculture, accurate and timely detection of plant diseases is crucial for minimizing crop losses and ensuring food security. Traditional methods of plant disease detection often rely on visual inspection and single-modal data analysis, which can be limited in their diagnostic accuracy. This study introduces an innovative ensemble learning framework that integrates Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for multimodal plant disease detection to address these limitations. The proposed framework capitalizes on the strengths of both CNNs and RNNs by processing visual data from leaf images and sequential data such as time-series measurements of environmental conditions. CNNs are adept at extracting intricate spatial features from leaf images, identifying visual symptoms of diseases with high precision. Concurrently, RNNs are designed to capture temporal patterns in sequential data, providing insights into environmental factors that may influence disease development. The ensemble method employed in this study aggregates predictions from both CNN and RNN models using techniques such as majority voting and weighted averaging. Majority voting involves combining the outputs of multiple models to make a final prediction based on the most common outcome, while weighted averaging assigns different weights to the predictions of each model based on their performance, leading to a more balanced and accurate diagnostic result. Experimental evaluations were conducted on comprehensive multimodal datasets, including diverse plant species and varying environmental conditions, to assess the effectiveness of the proposed framework. The results demonstrate that the ensemble approach significantly outperforms individual CNN and RNN models, achieving higher diagnostic accuracy, precision, recall, and F1 scores. This superior performance underscores the potential of integrating diverse data streams to provide a holistic view of plant health, enabling more accurate and reliable disease diagnosis. The findings of this study highlight the importance of leveraging multimodal data and advanced machine-learning techniques in plant disease detection. By integrating spatial and temporal information, the proposed framework offers a comprehensive diagnostic tool that can be instrumental in improving agricultural practices, optimizing plant health management, and ultimately contributing to sustainable farming practices.https://ieeexplore.ieee.org/document/10792887/Multimodal plant disease detectionconvolutional neural networks (CNNs)recurrent neural networks (RNNs)ensemble learning |
| spellingShingle | Anita Shrotriya Akhilesh Kumar Sharma Amit Kumar Bairwa R. Manoj Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection IEEE Access Multimodal plant disease detection convolutional neural networks (CNNs) recurrent neural networks (RNNs) ensemble learning |
| title | Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection |
| title_full | Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection |
| title_fullStr | Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection |
| title_full_unstemmed | Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection |
| title_short | Hybrid Ensemble Learning With CNN and RNN for Multimodal Cotton Plant Disease Detection |
| title_sort | hybrid ensemble learning with cnn and rnn for multimodal cotton plant disease detection |
| topic | Multimodal plant disease detection convolutional neural networks (CNNs) recurrent neural networks (RNNs) ensemble learning |
| url | https://ieeexplore.ieee.org/document/10792887/ |
| work_keys_str_mv | AT anitashrotriya hybridensemblelearningwithcnnandrnnformultimodalcottonplantdiseasedetection AT akhileshkumarsharma hybridensemblelearningwithcnnandrnnformultimodalcottonplantdiseasedetection AT amitkumarbairwa hybridensemblelearningwithcnnandrnnformultimodalcottonplantdiseasedetection AT rmanoj hybridensemblelearningwithcnnandrnnformultimodalcottonplantdiseasedetection |