A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process
The economic stability of many countries is closely tied to agriculture, where crop quality and yield are heavily affected by weather conditions and disease control. Unpredictable climate patterns and plant diseases remain major challenges, emphasizing the need for more reliable disease detection me...
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| Format: | Article |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003747 |
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| author | Mohammad Manzurul Islam Mst. Nasrat Jahan Niva Abdullahi Chowdhury Saleh Masum Rifat Ara Shams Taskeed Jabid Md. Sawkat Ali Md. Mostofa Kamal Rasel Muhammad Firoz Mridha |
| author_facet | Mohammad Manzurul Islam Mst. Nasrat Jahan Niva Abdullahi Chowdhury Saleh Masum Rifat Ara Shams Taskeed Jabid Md. Sawkat Ali Md. Mostofa Kamal Rasel Muhammad Firoz Mridha |
| author_sort | Mohammad Manzurul Islam |
| collection | DOAJ |
| description | The economic stability of many countries is closely tied to agriculture, where crop quality and yield are heavily affected by weather conditions and disease control. Unpredictable climate patterns and plant diseases remain major challenges, emphasizing the need for more reliable disease detection methods. Traditionally, plant disease identification has relied on visual examination, a method that is often inaccurate. To address this, our study proposes a two-stage model for improved disease detection in mango leaves. In the first stage, we implement an innovative, block-based feature extraction technique using Local Directional Patterns (LDP) and Local Directional Pattern variance (LDPv) on a comprehensive dataset, MangoLeafBD, consisting of 4000 images, achieving satisfactory results in terms of detection accuracy, sensitivity, specificity, and false negative rate. In the second stage, we introduce a Knowledge Distillation (KD) process to further enhance model performance by transferring knowledge from a larger teacher model to a smaller student model. Our results demonstrate significant advancement, with the KD-enhanced model achieving an improvement in detection accuracy from 89.2% to 95.6%, sensitivity from 7.8% to 4.1%, and specificity from 71.2% to 88.9% for Anthracnose disease. Similar improvements were observed in detecting other diseases, making our approach a more robust and efficient solution for mango plant disease detection. |
| format | Article |
| id | doaj-art-c5d49e73444247f29049abbf8528f364 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-c5d49e73444247f29049abbf8528f3642025-08-26T04:14:11ZengElsevierEcological Informatics1574-95412025-11-019110336510.1016/j.ecoinf.2025.103365A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation processMohammad Manzurul Islam0Mst. Nasrat Jahan Niva1Abdullahi Chowdhury2Saleh Masum3Rifat Ara Shams4Taskeed Jabid5Md. Sawkat Ali6Md. Mostofa Kamal Rasel7Muhammad Firoz Mridha8Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, Bangladesh; Corresponding author.Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, BangladeshDepartment of Information and Communication Engineering, University of Rajshahi, Charukala Road, Rajshahi 6205, BangladeshDepartment of Information and Communication Engineering, University of Rajshahi, Charukala Road, Rajshahi 6205, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka 1212, BangladeshDepartment of Computer Science, American International University Bangladesh, Khilkhet, Dhaka 1229, BangladeshThe economic stability of many countries is closely tied to agriculture, where crop quality and yield are heavily affected by weather conditions and disease control. Unpredictable climate patterns and plant diseases remain major challenges, emphasizing the need for more reliable disease detection methods. Traditionally, plant disease identification has relied on visual examination, a method that is often inaccurate. To address this, our study proposes a two-stage model for improved disease detection in mango leaves. In the first stage, we implement an innovative, block-based feature extraction technique using Local Directional Patterns (LDP) and Local Directional Pattern variance (LDPv) on a comprehensive dataset, MangoLeafBD, consisting of 4000 images, achieving satisfactory results in terms of detection accuracy, sensitivity, specificity, and false negative rate. In the second stage, we introduce a Knowledge Distillation (KD) process to further enhance model performance by transferring knowledge from a larger teacher model to a smaller student model. Our results demonstrate significant advancement, with the KD-enhanced model achieving an improvement in detection accuracy from 89.2% to 95.6%, sensitivity from 7.8% to 4.1%, and specificity from 71.2% to 88.9% for Anthracnose disease. Similar improvements were observed in detecting other diseases, making our approach a more robust and efficient solution for mango plant disease detection.http://www.sciencedirect.com/science/article/pii/S1574954125003747Detecting diseasesMango leavesMachine learningSpatio-temporal features |
| spellingShingle | Mohammad Manzurul Islam Mst. Nasrat Jahan Niva Abdullahi Chowdhury Saleh Masum Rifat Ara Shams Taskeed Jabid Md. Sawkat Ali Md. Mostofa Kamal Rasel Muhammad Firoz Mridha A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process Ecological Informatics Detecting diseases Mango leaves Machine learning Spatio-temporal features |
| title | A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| title_full | A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| title_fullStr | A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| title_full_unstemmed | A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| title_short | A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| title_sort | two stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process |
| topic | Detecting diseases Mango leaves Machine learning Spatio-temporal features |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125003747 |
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