Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor
The precise and prompt identification of plant diseases constitutes a crucial element in maintaining robust crop production, particularly with regard to ornamental and economically valuable species within the Malvaceae family. This study introduces an advanced deep learning-based methodology for the...
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| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01044.pdf |
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| author | Nichat Mangesh K. Yedey Sanjay |
| author_facet | Nichat Mangesh K. Yedey Sanjay |
| author_sort | Nichat Mangesh K. |
| collection | DOAJ |
| description | The precise and prompt identification of plant diseases constitutes a crucial element in maintaining robust crop production, particularly with regard to ornamental and economically valuable species within the Malvaceae family. This study introduces an advanced deep learning-based methodology for the identification of diseases in Malvaceae leaf images by incorporating a tailored Convolutional Neural Network (CNN) alongside Color Level Descriptor (CLD) feature extraction. The CLD technique enhances the input dataset by capturing spatial color attributes, thereby significantly augmenting the model's capability to differentiate between healthy and diseased leaf patterns. The system underwent training and validation on a meticulously curated dataset containing images of diverse species from the Malvaceae family, exhibiting enhanced accuracy and resilience relative to traditional CNN models. Experimental findings indicate that the integration of CLD facilitates more precise feature representation and superior classification efficacy. This innovative approach holds substantial promise for practical implementation in agricultural diagnostics, fostering early detection and effective management of plant diseases affecting the Malvaceae family. |
| format | Article |
| id | doaj-art-a44113f2254f4d7cb412ea2b7af04ba7 |
| institution | Kabale University |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-a44113f2254f4d7cb412ea2b7af04ba72025-08-20T03:30:56ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013280104410.1051/epjconf/202532801044epjconf_icetsf2025_01044Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level DescriptorNichat Mangesh K.0Yedey Sanjay1P.G. Department of Computer Science & Technology DCPE, HVPM and P R Pote Patil College of Engineering and ManagementP.G. Department of Computer Science & Technology DCPE, HVPMThe precise and prompt identification of plant diseases constitutes a crucial element in maintaining robust crop production, particularly with regard to ornamental and economically valuable species within the Malvaceae family. This study introduces an advanced deep learning-based methodology for the identification of diseases in Malvaceae leaf images by incorporating a tailored Convolutional Neural Network (CNN) alongside Color Level Descriptor (CLD) feature extraction. The CLD technique enhances the input dataset by capturing spatial color attributes, thereby significantly augmenting the model's capability to differentiate between healthy and diseased leaf patterns. The system underwent training and validation on a meticulously curated dataset containing images of diverse species from the Malvaceae family, exhibiting enhanced accuracy and resilience relative to traditional CNN models. Experimental findings indicate that the integration of CLD facilitates more precise feature representation and superior classification efficacy. This innovative approach holds substantial promise for practical implementation in agricultural diagnostics, fostering early detection and effective management of plant diseases affecting the Malvaceae family.https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01044.pdf |
| spellingShingle | Nichat Mangesh K. Yedey Sanjay Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor EPJ Web of Conferences |
| title | Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor |
| title_full | Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor |
| title_fullStr | Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor |
| title_full_unstemmed | Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor |
| title_short | Detection of Diseases in Malvaceae Family plants using Enhanced Deep Learning Algorithm with Color Level Descriptor |
| title_sort | detection of diseases in malvaceae family plants using enhanced deep learning algorithm with color level descriptor |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/13/epjconf_icetsf2025_01044.pdf |
| work_keys_str_mv | AT nichatmangeshk detectionofdiseasesinmalvaceaefamilyplantsusingenhanceddeeplearningalgorithmwithcolorleveldescriptor AT yedeysanjay detectionofdiseasesinmalvaceaefamilyplantsusingenhanceddeeplearningalgorithmwithcolorleveldescriptor |