Progressive Regional Spatial Mechanism With Convolutional Neural Network For Cotton Leaf Disease Classification
DOI:
https://doi.org/10.29284/nvsqkv16Keywords:
Background Images, Bacteria, Convolutional Neural Network, Cotton Leaf Disease, Intra Class Similarities, Progressive Regional Spatial Mechanism.Abstract
Cotton leaf disease is the infections affected cotton plant leaves produced though bacteria, fungi and viruses are leads to symptoms like discoloration and spots in the cotton leaves. The cotton leaf disease classification is procedure of recognizing and classifying the diseases from the leaf images based on leaf shapes and visual patterns. However, intra class similarities among diseases, complex background images and overlapping disease symptoms are leads to misclassification which reduce the model classification accuracy. To overcome these challenges, Progressive Regional Spatial Mechanism - Convolutional Neural Network (PRSM-CNN) is proposed to increase the spatial feature discrimination and efficiently classify the visual similarity among diseases which increases the model classification accuracy. The proposed PRSM-CNN increases classification accuracy though efficiently highlighting disease relevant regions while suppressing complex background noise and allowed reliable performance under noisy data augmentation conditions. The cotton leave disease and cotton disease datasets are used in this research, the proposed PRSM-CNN model attains the high level of accuracies of 99.12 % and 99.58% in cotton leaf disease and cotton disease datasets respectively.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
