ial-blight . Accessed 02 Mar 2022 27. Blast (leaf and collar)-IRRI Rice Knowledge Bank. http://www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/blast-leaf-collar . Accessed 02 Mar 2022 28. Sethy PK Rice leaf disease image samples Mendeley Data 2020 10.17632/FWCJ7STB8R.1 29. Leaf Rice Disease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022 30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022 31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . A
Open resource ↗Kaggle · leaf-rice-disease-indonesia · lines:474-517Unverified paper record
A novel GCL hybrid classification model for paddy diseases.
International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management · 19 Sept 2022 · 10.1007/s41870-022-01094-6
Abstract
The demand for agricultural products increased exponentially as the global population grew. The rapid development of computer vision-based artificial intelligence and deep learning-related technologies has impacted a wide range of industries, including disease detection and classification. This paper introduces a novel neural network-based hybrid model (GCL). GCL is a dataset-augmentation fusion of long-short term memory (LSTM) and convolutional neural network (CNN) with generative adversarial network (GAN). GAN is used for the augmentation of the dataset, CNN extracts the features and LSTM classifies the various paddy diseases. The GCL model is being investigated to improve the classification model's accuracy and reliability. The dataset was compiled using secondary resources such as Mendeley, Kaggle, UCI, and GitHub, having images of bacterial blight, leaf smut, and rice blast. The experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy. GCL can further be used for the classification of more diseases of paddy.
Plant phenotyping relevance
イネ葉画像から病害状態を分類する画像解析モデルを開発・評価しており、植物病害表現型の取得・分類手法が中心である。
abstractThis paper introduces a novel neural network-based hybrid model (GCL).
abstractCNN extracts the features and LSTM classifies the various paddy diseases.
abstractThe experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy.
Code and data availability
The paper's plant-phenotyping inputs are rice disease leaf images compiled from public repositories (Kaggle, UCI, GitHub; Mendeley also cited but its URL is not in the allowed list). Three of these public image datasets are directly used as the paper's raw phenotyping data and are actionable via allowed URLs. No author
gement/diseases/item/blast-leaf-collar . Accessed 02 Mar 2022 28. Sethy PK Rice leaf disease image samples Mendeley Data 2020 10.17632/FWCJ7STB8R.1 29. Leaf Rice Disease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022 30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022 31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . Accessed 02 Mar 2022
Open resource ↗UCI machine learning repository · Rice+Leaf+Diseases · lines:474-517ease|Kaggle. https://www.kaggle.com/tedisetiady/leaf-rice-disease-indonesia . Accessed 02 Mar 2022 30. UCI machine learning repository: rice leaf diseases data set. https://archive.ics.uci.edu/ml/datasets/Rice+Leaf+Diseases . Accessed 02 Mar 2022 31. GitHub-aldrin233/RiceDiseases-DataSet: Data Set for Rice Diseases with labels. https://github.com/aldrin233/RiceDiseases-DataSet . Accessed 02 Mar 2022
Open resource ↗GitHub-aldrin233/RiceDiseases-DataSet · RiceDiseases-DataSet · lines:474-517This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.