2023
DOI: 10.1371/journal.pone.0282250
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Palm tree disease detection and classification using residual network and transfer learning of inception ResNet

Abstract: Agriculture has become an essential field of study and is considered a challenge for many researchers in computer vision specialization. The early detection and classification of plant diseases are crucial for preventing growing diseases and hence yield reduction. Although many state-of-the-artwork proposed various classification techniques for plant diseases, still face many challenges such as noise reduction, extracting the relevant features, and excluding the redundant ones. Recently, deep learning models a… Show more

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Cited by 16 publications
(2 citation statements)
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“…Krishnamoorthy et al [15] combined Inception-ResNetv2, a pre-trained deep convolutional neural network, with a transfer learning approach for the identification of rice leaf diseases, the parameters of the model were optimized for the identification task, and finally a recognition accuracy of 95.67% was obtained. Ahmed et al [16] proposed two deep learning methods for palm leaf disease classification:Residual Network (ResNet) and Migration Learning with Inception ResNet. Both methods deal with variations in brightness and background, different scales of images, and inter-class similarity.…”
Section: Introductionmentioning
confidence: 99%
“…Krishnamoorthy et al [15] combined Inception-ResNetv2, a pre-trained deep convolutional neural network, with a transfer learning approach for the identification of rice leaf diseases, the parameters of the model were optimized for the identification task, and finally a recognition accuracy of 95.67% was obtained. Ahmed et al [16] proposed two deep learning methods for palm leaf disease classification:Residual Network (ResNet) and Migration Learning with Inception ResNet. Both methods deal with variations in brightness and background, different scales of images, and inter-class similarity.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning technology has emerged as a powerful tool for computer vision tasks, including disease detection and classification. (2,3) However, deep learning models face two primary challenges: the need for large, diverse datasets and the requirement of substantial computational resources for efficient results. Acquiring and curating such datasets can be challenging, and training deep learning models using these datasets can be computationally intensive and time-consuming.…”
Section: Introductionmentioning
confidence: 99%