A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases
Abstract Agriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge l...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-05-01
|
| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13640-021-00558-2 |
| _version_ | 1823926925446348800 |
|---|---|
| author | Muhammad Attique Khan Tallha Akram Muhammad Sharif Majed Alhaisoni Tanzila Saba Nadia Nawaz |
| author_facet | Muhammad Attique Khan Tallha Akram Muhammad Sharif Majed Alhaisoni Tanzila Saba Nadia Nawaz |
| author_sort | Muhammad Attique Khan |
| collection | DOAJ |
| description | Abstract Agriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy. |
| first_indexed | 2024-12-16T20:31:34Z |
| format | Article |
| id | doaj.art-f76b9bf6a8074866967d0c0b4b175911 |
| institution | Directory Open Access Journal |
| issn | 1687-5281 |
| language | English |
| last_indexed | 2024-12-16T20:31:34Z |
| publishDate | 2021-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Image and Video Processing |
| spelling | doaj.art-f76b9bf6a8074866967d0c0b4b1759112022-12-21T22:17:25ZengSpringerOpenEURASIP Journal on Image and Video Processing1687-52812021-05-012021112810.1186/s13640-021-00558-2A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseasesMuhammad Attique Khan0Tallha Akram1Muhammad Sharif2Majed Alhaisoni3Tanzila Saba4Nadia Nawaz5Department of Computer Science and Engineering, HITEC UniversityDepartment of ECE, COMSATS University Islamabad, WahCantt CampusDepartment of Computer Science, COMSATS University Islamabad, WahCantt CampusCollege of Computer Science and Engineering, University of HailCollege of Computer and Information Sciences, Prince Sultan UniversityDepartment of ECE, COMSATS University Islamabad, WahCantt CampusAbstract Agriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy.https://doi.org/10.1186/s13640-021-00558-2Contrast stretchingSegmentationFusionFeature extractionFeature selectionClassification |
| spellingShingle | Muhammad Attique Khan Tallha Akram Muhammad Sharif Majed Alhaisoni Tanzila Saba Nadia Nawaz A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases EURASIP Journal on Image and Video Processing Contrast stretching Segmentation Fusion Feature extraction Feature selection Classification |
| title | A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases |
| title_full | A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases |
| title_fullStr | A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases |
| title_full_unstemmed | A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases |
| title_short | A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases |
| title_sort | probabilistic segmentation and entropy rank correlation based feature selection approach for the recognition of fruit diseases |
| topic | Contrast stretching Segmentation Fusion Feature extraction Feature selection Classification |
| url | https://doi.org/10.1186/s13640-021-00558-2 |
| work_keys_str_mv | AT muhammadattiquekhan aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT tallhaakram aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT muhammadsharif aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT majedalhaisoni aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT tanzilasaba aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT nadianawaz aprobabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT muhammadattiquekhan probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT tallhaakram probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT muhammadsharif probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT majedalhaisoni probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT tanzilasaba probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases AT nadianawaz probabilisticsegmentationandentropyrankcorrelationbasedfeatureselectionapproachfortherecognitionoffruitdiseases |