Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification
<p dir="ltr">Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturist...
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2024
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| _version_ | 1864513547767119872 |
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| author | Mudassir Iftikhar (21323900) |
| author2 | Irfan Ali Kandhro (17541876) Neha Kausar (21323903) Asadullah Kehar (17541885) Mueen Uddin (4903510) Abdulhalim Dandoush (17541804) |
| author2_role | author author author author author |
| author_facet | Mudassir Iftikhar (21323900) Irfan Ali Kandhro (17541876) Neha Kausar (21323903) Asadullah Kehar (17541885) Mueen Uddin (4903510) Abdulhalim Dandoush (17541804) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mudassir Iftikhar (21323900) Irfan Ali Kandhro (17541876) Neha Kausar (21323903) Asadullah Kehar (17541885) Mueen Uddin (4903510) Abdulhalim Dandoush (17541804) |
| dc.date.none.fl_str_mv | 2024-06-06T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10462-024-10809-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Plant_disease_management_a_fine-tuned_enhanced_CNN_approach_with_mobile_app_integration_for_early_detection_and_classification/29022359 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Agricultural, veterinary and food sciences Agricultural biotechnology Agriculture, land and farm management Crop and pasture production Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Convolutional neural network Plant Disease Detection ANN Machine Learning |
| dc.title.none.fl_str_mv | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10809-z" target="_blank">https://dx.doi.org/10.1007/s10462-024-10809-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_50eac1f3bbfa4e2eafb73cf0edfaea3e |
| identifier_str_mv | 10.1007/s10462-024-10809-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29022359 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classificationMudassir Iftikhar (21323900)Irfan Ali Kandhro (17541876)Neha Kausar (21323903)Asadullah Kehar (17541885)Mueen Uddin (4903510)Abdulhalim Dandoush (17541804)Agricultural, veterinary and food sciencesAgricultural biotechnologyAgriculture, land and farm managementCrop and pasture productionEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceComputer vision and multimedia computationConvolutional neural networkPlant Disease DetectionANNMachine Learning<p dir="ltr">Farmers face the formidable challenge of meeting the increasing demands of a rapidly growing global population for agricultural products, while plant diseases continue to wreak havoc on food production. Despite substantial investments in disease management, agriculturists are increasingly turning to advanced technology for more efficient disease control. This paper addresses this critical issue through an exploration of a deep learning-based approach to disease detection. Utilizing an optimized Convolutional Neural Network (E-CNN) architecture, the study concentrates on the early detection of prevalent leaf diseases in Apple, Corn, and Potato crops under various conditions. The research conducts a thorough performance analysis, emphasizing the impact of hyperparameters on plant disease detection across these three distinct crops. Multiple machine learning and pre-trained deep learning models are considered, comparing their performance after fine-tuning their parameters. Additionally, the study investigates the influence of data augmentation on detection accuracy. The experimental results underscore the effectiveness of our fine-tuned enhanced CNN model, achieving an impressive 98.17% accuracy in fungal classes. This research aims to pave the way for more efficient plant disease management and, ultimately, to enhance agricultural productivity in the face of mounting global challenges. To improve accessibility for farmers, the developed model seamlessly integrates with a mobile application, offering immediate results upon image upload or capture. In case of a detected disease, the application provides detailed information on the disease, its causes, and available treatment options.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10809-z" target="_blank">https://dx.doi.org/10.1007/s10462-024-10809-z</a></p>2024-06-06T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10462-024-10809-zhttps://figshare.com/articles/journal_contribution/Plant_disease_management_a_fine-tuned_enhanced_CNN_approach_with_mobile_app_integration_for_early_detection_and_classification/29022359CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290223592024-06-06T03:00:00Z |
| spellingShingle | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification Mudassir Iftikhar (21323900) Agricultural, veterinary and food sciences Agricultural biotechnology Agriculture, land and farm management Crop and pasture production Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Convolutional neural network Plant Disease Detection ANN Machine Learning |
| status_str | publishedVersion |
| title | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| title_full | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| title_fullStr | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| title_full_unstemmed | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| title_short | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| title_sort | Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification |
| topic | Agricultural, veterinary and food sciences Agricultural biotechnology Agriculture, land and farm management Crop and pasture production Engineering Biomedical engineering Information and computing sciences Artificial intelligence Computer vision and multimedia computation Convolutional neural network Plant Disease Detection ANN Machine Learning |