Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach
Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-06-01
|
| Series: | Applied Computer Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/acss-2023-0009 |
| _version_ | 1797740377246007296 |
|---|---|
| author | Chaudhari Vandana Patil Manoj P. |
| author_facet | Chaudhari Vandana Patil Manoj P. |
| author_sort | Chaudhari Vandana |
| collection | DOAJ |
| description | Plant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages can drastically help in fending off the reduction in productivity/profit. Providing an intelligent and automated solution becomes a necessity. This can be achieved with the help of machine learning techniques. It involves a number of steps like image acquisition, image pre-processing using filtering and contrast enhancement techniques. Image segmentation, which is a crucial part in disease detection system, is done by applying genetic algorithm and the colour, texture features extracted using a local binary pattern. The novelty of this approach is applying the genetic algorithm for image segmentation and combining a set of propositions from all the learning classifiers with an ensemble method and calculating the results. This obeys the optimistic features of all the learning classifiers. System accuracy is evaluated using precision, recall, and accuracy measures. After analysing the results, it clearly shows that the ensemble models deliver very good accuracy of over 92 % as compared to an individual SVM, Naïve Bayes, and KNN classifiers. |
| first_indexed | 2024-03-12T14:11:33Z |
| format | Article |
| id | doaj.art-56b9fee8c3734e0183cb6f13d525c9d4 |
| institution | Directory Open Access Journal |
| issn | 2255-8691 |
| language | English |
| last_indexed | 2024-03-12T14:11:33Z |
| publishDate | 2023-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Computer Systems |
| spelling | doaj.art-56b9fee8c3734e0183cb6f13d525c9d42023-08-21T06:43:11ZengSciendoApplied Computer Systems2255-86912023-06-01281929910.2478/acss-2023-0009Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning ApproachChaudhari Vandana0Patil Manoj P.11Smt.G.G. Khadse College, Muktainagar, India2School of Computer Sciences, Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon, IndiaPlant diseases are a primary hazard to the productiveness of crops, which impacts food protection and decreases the profitability of farmers. Consequently, identification of plant diseases becomes a crucial task. By taking the right nurturing measures to remediate these diseases in the early stages can drastically help in fending off the reduction in productivity/profit. Providing an intelligent and automated solution becomes a necessity. This can be achieved with the help of machine learning techniques. It involves a number of steps like image acquisition, image pre-processing using filtering and contrast enhancement techniques. Image segmentation, which is a crucial part in disease detection system, is done by applying genetic algorithm and the colour, texture features extracted using a local binary pattern. The novelty of this approach is applying the genetic algorithm for image segmentation and combining a set of propositions from all the learning classifiers with an ensemble method and calculating the results. This obeys the optimistic features of all the learning classifiers. System accuracy is evaluated using precision, recall, and accuracy measures. After analysing the results, it clearly shows that the ensemble models deliver very good accuracy of over 92 % as compared to an individual SVM, Naïve Bayes, and KNN classifiers.https://doi.org/10.2478/acss-2023-0009bananadiseasesensemble learninglocal binary pattern |
| spellingShingle | Chaudhari Vandana Patil Manoj P. Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach Applied Computer Systems banana diseases ensemble learning local binary pattern |
| title | Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach |
| title_full | Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach |
| title_fullStr | Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach |
| title_full_unstemmed | Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach |
| title_short | Detection and Classification of Banana Leaf Disease Using Novel Segmentation and Ensemble Machine Learning Approach |
| title_sort | detection and classification of banana leaf disease using novel segmentation and ensemble machine learning approach |
| topic | banana diseases ensemble learning local binary pattern |
| url | https://doi.org/10.2478/acss-2023-0009 |
| work_keys_str_mv | AT chaudharivandana detectionandclassificationofbananaleafdiseaseusingnovelsegmentationandensemblemachinelearningapproach AT patilmanojp detectionandclassificationofbananaleafdiseaseusingnovelsegmentationandensemblemachinelearningapproach |