An Optimized Hyper Parameter Tuned Convolution Neural Frame for Potato Leaves Disease Prediction

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 2

Year of Publication : 2023

Authors : Anuj Rapaka, A. Clara Kanmani

10.14445/23488379/IJEEE-V10I2P117

How to Cite?

Anuj Rapaka, A. Clara Kanmani, "An Optimized Hyper Parameter Tuned Convolution Neural Frame for Potato Leaves Disease Prediction," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 2, pp. 180-195, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I2P117

Abstract:

Image processing is an exciting concept in several digital applications for identifying features precisely. Hence, this technology is chiefly utilized in agriculture applications to predict the disease affection in plat and leaves. However, the complex image has reduced the exactness rate of classification and segmentation. In this present work, the potato leaves are normal, and disease images are considered. Hence, a novel Ant Lion-based Hyper-Parameter tuned Convolution Neural Approach (ALHTCNA) has been designed with the required parameters to maximize the exactness score. Initially, the preprocessing function was activated to obtain error-free data. Consequently, the error-free data is moved to the classification frame, in that feature extraction, segmentation, and disease classification process has functioned. The considered disease types in the present work are Early-Blight (EB) and Late-Blight (LB). If the tested data did not contain these disease features, it is considered healthy leaves. Moreover, the planned design is implemented in the python platform, and the metrics are validated and compared with other schemes and has observed the finest segmentation and specification exactness score.

Keywords:

Image processing, Segmentation, Feature extraction, Potato leaves data, Disease specification.

References:

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