Rice Leaf Disease Detection Via Deep Neural Networks With Transfer Learning For Early Identification.
Rice is central to the lives of billions of people around the world. It is affected by different diseases at all growth periods of its cultivation. In plants, diseases are predominantly caused by biotic and abiotic components such as fungi, viroids, nematodes, bacteria, viruses, temperature, nutrien...
| Published in: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 1087 - 1098 |
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| Main Authors: | , |
| Format: | equations & formulas research tables/charts Journal Article |
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Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006069&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006069 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006069 151006069 151006069 151006069 ppf: 1087 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Rice Leaf Disease Detection Via Deep Neural Networks With Transfer Learning For Early Identification. aug: au: KRISHNAMOORTHY, N. PARAMESWARI, V. R. LOGA affil: Associate Professor, Kongu Engineering College, Department of Computer Science and Engineering, Perundurai, Erode, Tamilnadu, India sug: subj: Plant Diseases Plant Leaves Rice Early Diagnosis Neural Networks (Computer) Utilization Deep Learning Algorithms Bacterial and Fungal Diseases ab: Rice is central to the lives of billions of people around the world. It is affected by different diseases at all growth periods of its cultivation. In plants, diseases are predominantly caused by biotic and abiotic components such as fungi, viroids, nematodes, bacteria, viruses, temperature, nutrient deficiencies and other environmental conditions. The important diseases of rice such as leaf blast, bacterial blight, and brown spot cause damage to rice can greatly reduce yield. Farmers globally deal with the problem of plant diseases diagnosis and for their appropriate treatment. Therefore, real-time and precision identification of rice leaf diseases is urgently needed. Recent developments in DL (Deep Learning) approaches have tremendously increased the capabilities of visual recognition systems through computer vision technologies. The most popular DL models leverage for computer vision problems are convolutional neural networks (CNNs) that has confirmed very successful in areas such as image categorization, object detection, image segmentation, etc. It has paved the way for automatic plant disease detection using plant images. In this research, the dataset is limited to train a deep convolutional neural network models such as VGG-16, ResNet50 and InceptionV3 in this case transfer learning came into picture. Transfer learning is a popular technique in deep learning where pre-trained models are re-purposed on a related problem. The main purpose of this study is to assess deep convolutional neural networks with transfer learning for the identification of different diseases in the rice plant leaf. The proposed CNNs were utilized with transfer learning technique to lessen the training time and enhance the functional capabilities of neural networks. The classification accuracies for the VGG-16, ResNet50 and InceptionV3 CNN models were 87%, 93%and 95% respectively. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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