Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach
ABSTRACT Artificial intelligence, especially deep learning, has attracted significant interest in bioinformatics, with prominent applications in precision agriculture. A significant threat to the agricultural sector is the rapid propagation of diseases from affected to healthy plants, which, if unde...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13113 |
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| author | Muhammad Khalid Hamid Said Khalid Shah Ghassan Husnain Yazeed Yasin Ghadi Shahab Ahmad Al Maaytah Ayman Qahmash |
| author_facet | Muhammad Khalid Hamid Said Khalid Shah Ghassan Husnain Yazeed Yasin Ghadi Shahab Ahmad Al Maaytah Ayman Qahmash |
| author_sort | Muhammad Khalid Hamid |
| collection | DOAJ |
| description | ABSTRACT Artificial intelligence, especially deep learning, has attracted significant interest in bioinformatics, with prominent applications in precision agriculture. A significant threat to the agricultural sector is the rapid propagation of diseases from affected to healthy plants, which, if undetected, may culminate in significant crop losses. This research focusses on employing multi‐model deep‐learning techniques to identify diseases in the leaves of economically significant crops that are potatoes, tomatoes, grapes, apples, and peaches. These crops are widely grown and crucial for food security, with disease outbreaks threatening yield and quality. This study evaluates the performance of deep learning models, including VGG16, MobileNetV2, Xception, and ResNet, using four metrics, that is, Accuracy, Precision, Recall, and F1‐Score. Furthermore, consumer research was undertaken to evaluate user trust in AI‐driven multi‐model systems, collecting feedback from farmers to inform future research directions. The results demonstrate that the VGG16 model outperformed all others in every evaluation criterion. Experimental simulations were performed in Jupyter Notebook utilizing Anaconda and Python. The findings indicate that the proposed multi‐model approach allows a scalable, non‐invasive, and contactless machine vision solution for the early detection of diseases in plant leaves, achieving an efficiency of 99% via multimodal classification techniques that incorporate statistical variables including mean, median, mode, skewness, and kurtosis. |
| format | Article |
| id | doaj-art-d79d83ad0bf94dfd86d36b128f24e5b5 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-d79d83ad0bf94dfd86d36b128f24e5b52025-01-31T00:22:49ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13113Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel ApproachMuhammad Khalid Hamid0Said Khalid Shah1Ghassan Husnain2Yazeed Yasin Ghadi3Shahab Ahmad Al Maaytah4Ayman Qahmash5Department of Computer Science Bannu University of Science & Technology Bannu Khyber‐Pakhtunkhwa PakistanDepartment of Computer Science Bannu University of Science & Technology Bannu Khyber‐Pakhtunkhwa PakistanDepartment of Computer Science CECOS University of IT and Emerging Sciences Peshawar PakistanDepartment of Computer Science and Software Engineering Al Ain University Al Ain UAEDepartment of Languages and Humanities, Applied College King Faisal University Alhafof The Eastern Province Saudi ArabiaDepartment of Informatics and Computer Systems King Khalid University Abha Saudi ArabiaABSTRACT Artificial intelligence, especially deep learning, has attracted significant interest in bioinformatics, with prominent applications in precision agriculture. A significant threat to the agricultural sector is the rapid propagation of diseases from affected to healthy plants, which, if undetected, may culminate in significant crop losses. This research focusses on employing multi‐model deep‐learning techniques to identify diseases in the leaves of economically significant crops that are potatoes, tomatoes, grapes, apples, and peaches. These crops are widely grown and crucial for food security, with disease outbreaks threatening yield and quality. This study evaluates the performance of deep learning models, including VGG16, MobileNetV2, Xception, and ResNet, using four metrics, that is, Accuracy, Precision, Recall, and F1‐Score. Furthermore, consumer research was undertaken to evaluate user trust in AI‐driven multi‐model systems, collecting feedback from farmers to inform future research directions. The results demonstrate that the VGG16 model outperformed all others in every evaluation criterion. Experimental simulations were performed in Jupyter Notebook utilizing Anaconda and Python. The findings indicate that the proposed multi‐model approach allows a scalable, non‐invasive, and contactless machine vision solution for the early detection of diseases in plant leaves, achieving an efficiency of 99% via multimodal classification techniques that incorporate statistical variables including mean, median, mode, skewness, and kurtosis.https://doi.org/10.1002/eng2.13113agronomistartificial intelligencebackpropagation algorithmconvolutional neural networkdeep learningprecision agriculture |
| spellingShingle | Muhammad Khalid Hamid Said Khalid Shah Ghassan Husnain Yazeed Yasin Ghadi Shahab Ahmad Al Maaytah Ayman Qahmash Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach Engineering Reports agronomist artificial intelligence backpropagation algorithm convolutional neural network deep learning precision agriculture |
| title | Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach |
| title_full | Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach |
| title_fullStr | Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach |
| title_full_unstemmed | Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach |
| title_short | Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach |
| title_sort | enhancing agricultural disease detection a multi model deep learning novel approach |
| topic | agronomist artificial intelligence backpropagation algorithm convolutional neural network deep learning precision agriculture |
| url | https://doi.org/10.1002/eng2.13113 |
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