CNN-RNN Deep Learning Networks for Pattern Recognition Problems

Imran, Inas Ismael

doi:10.1109/icbats57792.2023.10111363

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…In recent years, there has been significant progress in the development of the convolutional neural network method, which has the capability to autonomously identify and extract discriminative features for the task of image classification [4]. The convolutional neural network (CNN) is well recognized as an outstanding technique for recognition of patterns [5] and expression identification [6] in different fields.…”

Section: Introductionmentioning

confidence: 99%

Guava fruit disease identification based on improved convolutional neural network

Mahamudul Hashan,

Tariqur Rahman,

Avinash

et al. 2024

IJECE

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Guava fruit cultivation is crucial for Asian economic development, with Indonesia producing 449,970 metric tons between 2022 and 2023. However, technology-based approaches can detect disease symptoms, enhancing production and mitigating economic losses by enhancing quality. In this paper, we introduce an accurate guava fruit disease detection (GFDI) system. It contains the generation of appropriate diseased images and the development of a novel improved convolutional neural network (improved-CNN) that is built depending on the principles of AlexNet. Also, several preprocessing techniques have been used, including data augmentation, contrast enhancement, image resizing, and dataset splitting. The proposed improved-CNN model is trained to identify three common guava fruit diseases using a dataset of 612 images. The experimental findings indicate that the proposed improved-CNN model achieve accuracy 98% for trains and 93% for tests using 0.001 learning rate, the model parameters are decreased by 50,106,831 compared with traditional AlexNet model. The findings of the investigation indicate that the deep learning model improves the accuracy and convergence rate for guava fruit disease prevention.

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Section: Introductionmentioning

confidence: 99%