Analysis of banana plant health using machine learning techniques
The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of e...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Research
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113460/ http://psasir.upm.edu.my/id/eprint/113460/1/113460.pdf |
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| author | Thiagarajan, Joshva Devadas Kulkarni, Siddharaj Vitthal Jadhav, Shreyas Anil Waghe, Ayush Ashish Raja, S.P. Rajagopal, Sivakumar Poddar, Harshit Subramaniam, Shamala |
| author_facet | Thiagarajan, Joshva Devadas Kulkarni, Siddharaj Vitthal Jadhav, Shreyas Anil Waghe, Ayush Ashish Raja, S.P. Rajagopal, Sivakumar Poddar, Harshit Subramaniam, Shamala |
| author_sort | Thiagarajan, Joshva Devadas |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research. |
| first_indexed | 2025-11-15T14:17:18Z |
| format | Article |
| id | upm-113460 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:17:18Z |
| publishDate | 2024 |
| publisher | Nature Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1134602024-11-25T06:43:49Z http://psasir.upm.edu.my/id/eprint/113460/ Analysis of banana plant health using machine learning techniques Thiagarajan, Joshva Devadas Kulkarni, Siddharaj Vitthal Jadhav, Shreyas Anil Waghe, Ayush Ashish Raja, S.P. Rajagopal, Sivakumar Poddar, Harshit Subramaniam, Shamala The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research. Nature Research 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/113460/1/113460.pdf Thiagarajan, Joshva Devadas and Kulkarni, Siddharaj Vitthal and Jadhav, Shreyas Anil and Waghe, Ayush Ashish and Raja, S.P. and Rajagopal, Sivakumar and Poddar, Harshit and Subramaniam, Shamala (2024) Analysis of banana plant health using machine learning techniques. Scientific Reports, 14 (1). art. no. 15041. pp. 1-23. ISSN 2045-2322; eISSN: 2045-2322 https://www.nature.com/articles/s41598-024-63930-y?error=cookies_not_supported&code=e399510b-1dbb-4ae4-b8bb-afbaef301123 10.1038/s41598-024-63930-y |
| spellingShingle | Thiagarajan, Joshva Devadas Kulkarni, Siddharaj Vitthal Jadhav, Shreyas Anil Waghe, Ayush Ashish Raja, S.P. Rajagopal, Sivakumar Poddar, Harshit Subramaniam, Shamala Analysis of banana plant health using machine learning techniques |
| title | Analysis of banana plant health using machine learning techniques |
| title_full | Analysis of banana plant health using machine learning techniques |
| title_fullStr | Analysis of banana plant health using machine learning techniques |
| title_full_unstemmed | Analysis of banana plant health using machine learning techniques |
| title_short | Analysis of banana plant health using machine learning techniques |
| title_sort | analysis of banana plant health using machine learning techniques |
| url | http://psasir.upm.edu.my/id/eprint/113460/ http://psasir.upm.edu.my/id/eprint/113460/ http://psasir.upm.edu.my/id/eprint/113460/ http://psasir.upm.edu.my/id/eprint/113460/1/113460.pdf |