Use of RESNET-50 Neural Network in Diagnosing Diseases Mango Leaves

Djarot Hindarto

doi:10.47709/cnahpc.v6i1.3308

Authors

Djarot Hindarto Universitas Nasional

DOI:

10.47709/cnahpc.v6i1.3308

Keywords:

Agricultural Application, Convolutional Neural Network, Disease Mango Leaves, RestNet-50, Feature Extraction

Dimension Badge Record

Abstract

Using a state-of-the-art convolutional neural network, specifically RESNET-50, for disease diagnosis on mango leaves is the focus of this research. The end goal is to develop a trustworthy method of mango plant disease detection using leaf image analysis. The approach used comprised gathering a sizable dataset encompassing a range of mango leaf diseases. Afterward, a classification system was developed by training the RESNET-50 model on image data. The system is able to learn extraordinarily intricate and profound visual patterns in pictures of mango leaves thanks to RESNET-50's deep and complicated architecture, which improves feature extraction. With a Test Accuracy of 99.16% and a Test Loss of only 0.4332, the results demonstrate a very reliable system. This impressive level of precision verifies that the system is capable of correctly distinguishing and categorizing mango leaf diseases. Consequently, this case demonstrates promising agricultural applications of the RESNET-50 model and offers a dependable and effective means of disease detection in mango plants. This study adds to the growing body of knowledge that can aid agricultural professionals and farmers in the early detection of disease symptoms on mango leaves, allowing for the prompt implementation of preventative measures. These findings also have broader implications, such as the potential for better agricultural productivity and management brought about by the use of comparable technologies for disease analysis in different crops.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 259 times

References

Ahmed, R., Hossain, T., Ahnaf, A., & Kabir, A. (2023). LeafNet?: A proficient convolutional neural network for detecting seven prominent mango leaf diseases. 14(August).

Ding, R., Qiao, Y., Yang, X., Zhang, Y., Huang, Z., Wang, D., & Liu, H. (2022). Improved ResNet Based Apple Leaf Diseases Identification. 32.

Durga, B. K., & Rajesh, V. (2022). A ResNet deep learning based facial recognition design for future multimedia applications. Computers and Electrical Engineering, 104(PA), 108384. https://doi.org/10.1016/j.compeleceng.2022.108384

Hindarto, D. (n.d.). Comparison of Detection with Transfer Learning Architecture RestNet18, RestNet50, RestNet101 on Corn Leaf Disease. 41–48.

Hindarto, D. (2023). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. 8(4), 2810–2818.

Ji, L., Wei, Z., Hao, J., & Wang, C. (2023). An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet. 242(August).

Kumar, M., Sharma, A., Bajpai, V., & Narayan, B. (2022). Encoder and decoder network with ResNet-50 and global average feature pooling for local change detection. 222(July).

Mirza, A. F., Mansoor, M., Usman, M., & Ling, Q. (2023). Hybrid Inception-embedded deep neural network ResNet for short and. 294(August).

Reddy, S. R. G., Varma, G. P. S., & Lakshmi, R. (2023). Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification. 105(May 2022), 1–15.

Suherman, E., Hindarto, D., Makmur, A., & Santoso, H. (2023). Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection. Sinkron, 8(1), 247–255. https://doi.org/10.33395/sinkron.v8i1.11944

Suherman, E., Rahman, B., Hindarto, D., & Santoso, H. (2023). Implementation of ResNet-50 on End-to-End Object Detection ( DETR ) on Objects. 8(2), 1085–1097.

Talasila, S., Rawal, K., Sethi, G., & Mss, S. (2022). Black gram Plant Leaf Disease ( BPLD ) dataset for recognition and classification of diseases using computer-vision algorithms. 45.

Tian, T., Wang, L., Luo, M., Sun, Y., & Liu, X. (2022). ResNet-50 based technique for EEG image characterization due to varying environmental stimuli. 225.

Wang, Y., & Wu, J. (2023). Resnet-based power system frequency security assessment considering frequency spatiotemporal distribution characteristics. 9, 125–134.

Wiku, L., Wang, P., Noh, H., Jung, H., Jung, D., & Han, X. (2023). Airborne hyperspectral imaging for early diagnosis of kimchi cabbage downy mildew using 3D-ResNet and leaf segmentation. 214(September).

Xiao, Q., Wang, Y., Fan, J., Yi, Z., Hong, H., Xie, X., Huang, Q., Fu, J., Ouyang, J., Zhao, X., Wang, Z., & Zhu, Z. (2024). A computer vision and residual neural network ( ResNet ) combined method for automated and accurate yeast replicative aging analysis of high-throughput microfluidic single-cell images. 244(October 2023), 1–13.

Xie, L., Han, B., Hu, X., & Bai, N. (2023). 2D magnetotelluric inversion based on ResNet. 4(August), 119–127.