Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree
Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy fi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Information Processing in Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214317324000647 |
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| author | A. Pushpa Athisaya Sakila Rani N. Suresh Singh |
| author_facet | A. Pushpa Athisaya Sakila Rani N. Suresh Singh |
| author_sort | A. Pushpa Athisaya Sakila Rani |
| collection | DOAJ |
| description | Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively. |
| format | Article |
| id | doaj-art-edb6d0611d604a5ab2b92d15e7df2429 |
| institution | Kabale University |
| issn | 2214-3173 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Information Processing in Agriculture |
| spelling | doaj-art-edb6d0611d604a5ab2b92d15e7df24292025-08-20T03:53:52ZengElsevierInformation Processing in Agriculture2214-31732025-06-0112223224410.1016/j.inpa.2024.09.003Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision treeA. Pushpa Athisaya Sakila Rani0N. Suresh Singh1Department of Computer Science, Malankara Catholic College, Manonmaniam Sundaranar University, Tamilnadu, India; Corresponding author.Department of Computer Applications, Malankara Catholic College, Manonmaniam Sundaranar University, Tamilnadu, IndiaPest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.http://www.sciencedirect.com/science/article/pii/S2214317324000647Empirical mode decompositionGray level co-occurrence matrixPrincipal component analysisDecision tree classifierEntropy filtering |
| spellingShingle | A. Pushpa Athisaya Sakila Rani N. Suresh Singh Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree Information Processing in Agriculture Empirical mode decomposition Gray level co-occurrence matrix Principal component analysis Decision tree classifier Entropy filtering |
| title | Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree |
| title_full | Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree |
| title_fullStr | Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree |
| title_full_unstemmed | Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree |
| title_short | Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree |
| title_sort | classification and identification of pest diseases and nutrient deficiency in paddy using layer based emd phase features with decision tree |
| topic | Empirical mode decomposition Gray level co-occurrence matrix Principal component analysis Decision tree classifier Entropy filtering |
| url | http://www.sciencedirect.com/science/article/pii/S2214317324000647 |
| work_keys_str_mv | AT apushpaathisayasakilarani classificationandidentificationofpestdiseasesandnutrientdeficiencyinpaddyusinglayerbasedemdphasefeatureswithdecisiontree AT nsureshsingh classificationandidentificationofpestdiseasesandnutrientdeficiencyinpaddyusinglayerbasedemdphasefeatureswithdecisiontree |