2021
DOI: 10.7717/peerj-cs.432
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Abstract: The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consumi… Show more

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Cited by 43 publications
(16 citation statements)
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References 55 publications
(58 reference statements)
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“…In this work, various TL models viz. InceptionResNetV2, InceptionV3, ResNet152V2, VGG19, DenseNet201 and Xception were fine-tuned with rice nutrition deficiency datasets [42][43][44][45][46][47]. The classification layer of each CNN pre-trained model was replaced with a pooling layer, a dense layer, and a softmax layer with the number of classes, which are 3 for the Kaggle dataset and 4 for the Mendeley dataset in this study.…”
Section: Transfer Learning Modelsmentioning
confidence: 99%
“…In this work, various TL models viz. InceptionResNetV2, InceptionV3, ResNet152V2, VGG19, DenseNet201 and Xception were fine-tuned with rice nutrition deficiency datasets [42][43][44][45][46][47]. The classification layer of each CNN pre-trained model was replaced with a pooling layer, a dense layer, and a softmax layer with the number of classes, which are 3 for the Kaggle dataset and 4 for the Mendeley dataset in this study.…”
Section: Transfer Learning Modelsmentioning
confidence: 99%
“…Zhou et al proposed a progressive detection model for vegetable disease through locating the interested region first, which provided an impressive perspective that it was possible to achieve superior results with the help of innovative model structure [ 22 ]. Moreover, Bari et al put the faster region convolutional neural network (Faster-RCNN) into application to diagnose the rice leaf disease [ 23 ]. Different from the whole image classification, the capability which was displaying the disease location further improved the identification accuracy to 99.25%.…”
Section: Introductionmentioning
confidence: 99%
“… Li et al (2020) proposed a video detection architecture based on deep learning and custom backbone, which was used for detecting rice diseases and pests in videos. Bari et al (2021) added the RPN structure into the Faster R-CNN algorithm to accurately locate the target position for generating candidate regions, which had a good detection effect on three diseases on rice leaves of one plant. Daniya and Vigneshwari (2021) proposed a deep neural network model to detect rice diseases.…”
Section: Introductionmentioning
confidence: 99%