Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach
Agriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification wer...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9211700 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558540405866496 |
|---|---|
| author | Arun Malik Gayatri Vaidya Vishal Jagota Sathyapriya Eswaran Akash Sirohi Isha Batra Manik Rakhra Evans Asenso |
| author_facet | Arun Malik Gayatri Vaidya Vishal Jagota Sathyapriya Eswaran Akash Sirohi Isha Batra Manik Rakhra Evans Asenso |
| author_sort | Arun Malik |
| collection | DOAJ |
| description | Agriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification were carried out by hand, but this was a time-consuming operation. Nowadays, deep learning algorithms are frequently employed for recognition and classification tasks. As a result, this manuscript investigates the diseases of sunflower leaves, specifically Alternaria leaf blight, Phoma blight, downy mildew, and Verticillium wilt, and proposes a hybrid model for the recognition and classification of sunflower diseases using deep learning techniques. VGG-16 and MobileNet are two transfer learning models that are used for classification purposes, and the stacking ensemble learning approach is used to merge them or create a hybrid model from the two models. This work makes use of a data set that was built by the author with the assistance of Google Images and comprises 329 images of sunflowers divided into five categories. On the basis of accuracy, a comparison is made between several existing deep learning models and the proposed model using the same data set as the original comparison. |
| format | Article |
| id | doaj-art-843c7d6c703b4896819ddbcd8658d16d |
| institution | Kabale University |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-843c7d6c703b4896819ddbcd8658d16d2025-02-03T01:32:06ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9211700Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning ApproachArun Malik0Gayatri Vaidya1Vishal Jagota2Sathyapriya Eswaran3Akash Sirohi4Isha Batra5Manik Rakhra6Evans Asenso7Department of Computer Science and EngineeringDepartment of Studies in Food TechnologyDepartment of Mechanical EngineeringDepartment of Agricultural ExtensionDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Computer Science and EngineeringDepartment of Agricultural EngineeringAgriculture and plants, which are a component of a nation's internal economy, play an important role in boosting the economy of that country. It becomes critical to preserve plants from infection at an early stage in order to be able to treat them. Previously, recognition and classification were carried out by hand, but this was a time-consuming operation. Nowadays, deep learning algorithms are frequently employed for recognition and classification tasks. As a result, this manuscript investigates the diseases of sunflower leaves, specifically Alternaria leaf blight, Phoma blight, downy mildew, and Verticillium wilt, and proposes a hybrid model for the recognition and classification of sunflower diseases using deep learning techniques. VGG-16 and MobileNet are two transfer learning models that are used for classification purposes, and the stacking ensemble learning approach is used to merge them or create a hybrid model from the two models. This work makes use of a data set that was built by the author with the assistance of Google Images and comprises 329 images of sunflowers divided into five categories. On the basis of accuracy, a comparison is made between several existing deep learning models and the proposed model using the same data set as the original comparison.http://dx.doi.org/10.1155/2022/9211700 |
| spellingShingle | Arun Malik Gayatri Vaidya Vishal Jagota Sathyapriya Eswaran Akash Sirohi Isha Batra Manik Rakhra Evans Asenso Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach Journal of Food Quality |
| title | Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach |
| title_full | Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach |
| title_fullStr | Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach |
| title_full_unstemmed | Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach |
| title_short | Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach |
| title_sort | design and evaluation of a hybrid technique for detecting sunflower leaf disease using deep learning approach |
| url | http://dx.doi.org/10.1155/2022/9211700 |
| work_keys_str_mv | AT arunmalik designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT gayatrivaidya designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT vishaljagota designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT sathyapriyaeswaran designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT akashsirohi designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT ishabatra designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT manikrakhra designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach AT evansasenso designandevaluationofahybridtechniquefordetectingsunflowerleafdiseaseusingdeeplearningapproach |