Image-based disease detection and classification in Indian apple plant species by using deep learning
Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural product...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | UMS Journal (OJS) |
| Language: | eng |
| Published: |
Universitas Muhammadiyah Surakarta
2022
|
| Subjects: | |
| Online Access: | https://journals2.ums.ac.id/index.php/arstech/article/view/1021 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1805340799365283840 |
|---|---|
| author | Wani, Sidrah Fayaz Ashraf, Arselan Sophian, Ali |
| author_facet | Wani, Sidrah Fayaz Ashraf, Arselan Sophian, Ali |
| author_sort | Wani, Sidrah Fayaz |
| collection | OJS |
| description | Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural productivity growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing an intelligent automated technique for plant disease detection is explored in this research. Deep learning is used to create a smart system for image-based disease detection in Indian apple plant species. Specifically, this study uses a convolution neural network architecture, ResNet-34, to identify diseases in apple plants. Based on 70-30% and 80-20% dataset partition, the proposed model obtained an accuracy of 97.5% and 98.4%, respectively. The results obtained from this study illustrate the productive exploration and utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that provide cure and preventative measures for the detected diseases. |
| format | UMS Journal (OJS) |
| id | oai:ojs2.journals2.ums.ac.id:article-1021 |
| institution | Universitas Muhammadiyah Surakarta |
| language | eng |
| publishDate | 2022 |
| publisher | Universitas Muhammadiyah Surakarta |
| record_format | ojs |
| spelling | oai:ojs2.journals2.ums.ac.id:article-1021 Image-based disease detection and classification in Indian apple plant species by using deep learning Wani, Sidrah Fayaz Ashraf, Arselan Sophian, Ali Convolutional neural networks Deep learning Plant disease detection Smart agriculture Plant diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Traditional farming methods are insufficient to address the impending global food crises. As a result, agricultural productivity growth is critical, and new techniques and methods are required for efficient and sustainable farming practices that balance the supply chain according to customer demand. Even though India is one of the most agriculturally dependent countries, it nevertheless suffers from various agricultural shortages. Plant diseases that go unnoticed and untreated are one such deprivation. Developing an intelligent automated technique for plant disease detection is explored in this research. Deep learning is used to create a smart system for image-based disease detection in Indian apple plant species. Specifically, this study uses a convolution neural network architecture, ResNet-34, to identify diseases in apple plants. Based on 70-30% and 80-20% dataset partition, the proposed model obtained an accuracy of 97.5% and 98.4%, respectively. The results obtained from this study illustrate the productive exploration and utility of the proposed model for future research by implementing various deep learning models and incorporating additional modules that provide cure and preventative measures for the detected diseases. Universitas Muhammadiyah Surakarta 2022-08-26 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://journals2.ums.ac.id/index.php/arstech/article/view/1021 10.23917/arstech.v3i1.1021 Applied Research and Smart Technology (ARSTech); Vol. 3 No. 1 (2022): Applied Research and Smart Technology; 38-48 2722-9645 2722-9637 eng https://journals2.ums.ac.id/index.php/arstech/article/view/1021/299 Copyright (c) 2022 Sidrah Fayaz Wani, Arselan Ashraf, Ali Sophian https://creativecommons.org/licenses/by/4.0 |
| spellingShingle | Convolutional neural networks Deep learning Plant disease detection Smart agriculture Wani, Sidrah Fayaz Ashraf, Arselan Sophian, Ali Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title | Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title_full | Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title_fullStr | Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title_full_unstemmed | Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title_short | Image-based disease detection and classification in Indian apple plant species by using deep learning |
| title_sort | image based disease detection and classification in indian apple plant species by using deep learning |
| topic | Convolutional neural networks Deep learning Plant disease detection Smart agriculture |
| topic_facet | Convolutional neural networks Deep learning Plant disease detection Smart agriculture |
| url | https://journals2.ums.ac.id/index.php/arstech/article/view/1021 |
| work_keys_str_mv | AT wanisidrahfayaz imagebaseddiseasedetectionandclassificationinindianappleplantspeciesbyusingdeeplearning AT ashrafarselan imagebaseddiseasedetectionandclassificationinindianappleplantspeciesbyusingdeeplearning AT sophianali imagebaseddiseasedetectionandclassificationinindianappleplantspeciesbyusingdeeplearning |