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All Journal International Journal of Electrical and Computer Engineering
Dey, Subhankar
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Computer Science & IT Electrical & Electronics Engineering

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Guava fruit disease identification based on improved convolutional neural network Mahamudul Hashan, Antor; Tariqur Rahman, Shaon Md; Avinash, Kumar; Ul Islam, Rizu Md Rakib; Dey, Subhankar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1544-1551

Abstract

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.
Co-Authors Avinash, Kumar Mahamudul Hashan, Antor Tariqur Rahman, Shaon Md Ul Islam, Rizu Md Rakib