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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18996385 |
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| _version_ | 1866901183882330112 |
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| author | Dr. SHANTHI AL. |
| author_facet | Dr. SHANTHI AL. |
| contents | <p>Rice, which is extensively utilized as a staple food across the globe, assumes <br>significant importance in the quest for superior rice products. However, the incidence of rice <br>illnesses may prevent rice-based goods from being produced at their best and of the highest <br>quality. Precision agriculture investigates the development of automated disease detection <br>and categorization systems in great detail. Because rice is grown in vast, moist regions, it <br>might be difficult to detect these infections. A Deep Learning-based multiclass paddy disease <br>finding model (DL-MPDP) is presented in this research, for accurate identification and <br>categorization of afflicted areas in paddy plants. IoT cameras are utilized to acquire <br>unprocessed images of paddy fields. The images undergo preprocessing techniques such as <br>LANCZOS, CLAHE, and Wavelet to increase their quality before additional analysis. Then, <br>the pre-processed image can be subjected to Feature Extraction (Texture Feature, Shape <br>Feature, and Edge Feature) via Onehot Encoding Technique. Then, the dimensions of the <br>extracted image will be reduced via a new Kernel-based Principal Component Analysis <br>(KPCA). Subsequently, from the dimensionally reduced data, the optimal features will be <br>provided via the newly modified Flower Pollination Algorithm (mFPA). The Recurrent <br>Neural Network with Long Short-Term Memory (HRNNLSTM) model that makes the final <br>detection (presence or absence of disease) is trained using the selected optimal features. <br>Moreover, to enhance the detection accuracy, the weight function of HRNNLSTM is fine<br>tuned using the Crayfish Optimization Algorithm.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18996385 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Precision Agriculture through Hybrid Deep Learning Ensembles and Advanced Optimization for Multiclass Paddy Disease Detection Dr. SHANTHI AL. <p>Rice, which is extensively utilized as a staple food across the globe, assumes <br>significant importance in the quest for superior rice products. However, the incidence of rice <br>illnesses may prevent rice-based goods from being produced at their best and of the highest <br>quality. Precision agriculture investigates the development of automated disease detection <br>and categorization systems in great detail. Because rice is grown in vast, moist regions, it <br>might be difficult to detect these infections. A Deep Learning-based multiclass paddy disease <br>finding model (DL-MPDP) is presented in this research, for accurate identification and <br>categorization of afflicted areas in paddy plants. IoT cameras are utilized to acquire <br>unprocessed images of paddy fields. The images undergo preprocessing techniques such as <br>LANCZOS, CLAHE, and Wavelet to increase their quality before additional analysis. Then, <br>the pre-processed image can be subjected to Feature Extraction (Texture Feature, Shape <br>Feature, and Edge Feature) via Onehot Encoding Technique. Then, the dimensions of the <br>extracted image will be reduced via a new Kernel-based Principal Component Analysis <br>(KPCA). Subsequently, from the dimensionally reduced data, the optimal features will be <br>provided via the newly modified Flower Pollination Algorithm (mFPA). The Recurrent <br>Neural Network with Long Short-Term Memory (HRNNLSTM) model that makes the final <br>detection (presence or absence of disease) is trained using the selected optimal features. <br>Moreover, to enhance the detection accuracy, the weight function of HRNNLSTM is fine<br>tuned using the Crayfish Optimization Algorithm.</p> |
| title | Precision Agriculture through Hybrid Deep Learning Ensembles and Advanced Optimization for Multiclass Paddy Disease Detection |
| url | https://doi.org/10.5281/zenodo.18996385 |