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Multi‐gradient‐direction Based Deep Learning Model for Arecanut Disease Identification by S. B. Mallikarjuna; Palaiahnakote Shivakumara; Vijeta Khare; M. Basavanna; Umapada Pal; B. Poornima is a Computer Science article available to read on EtoBox.

What is Multi‐gradient‐direction Based Deep Learning Model for Arecanut Disease Identification about?

Arecanut disease identification is a challenging problem in the field of image processing. In this work, we present a new combination of multi-gradient-direction and deep convolutional neural networks for arecanut disease identification, namely, rot, split and rotsplit. Due to the effect of the disease, there are chances of losing vital details in the images. To enhance the fine details in the images affected by diseases, we explore multi-Sobel directional masks for convolving with the input image, which results in enhanced images. The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding. Further, the features are extracted for foreground information for disease identification by exploring the ResNet architecture. The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images. Experimental results on the dataset of four classes (healthy, rot, split and rot-split) show that the proposed model is superior in terms of classification rate. K E Y W O R D S deep learning, image analysis, pattern recognition | INTRODUCTION From the ancient times arecanuts (also known as betel nuts) are tradition

Who reads Multi‐gradient‐direction Based Deep Learning Model for Arecanut Disease Identification?

Author

S. B. Mallikarjuna; Palaiahnakote Shivakumara; Vijeta Khare; M. Basavanna; Umapada Pal; B. Poornima

Publisher

Institution of Engineering and Technology (IET)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)