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Leaf Disease Detection in Banana Plant using Gabor Extraction and Region-Based Convolution Neural Network (RCNN)

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Abstract

Disease identification in bananas has proven to be more difficult in the field due to the fact that it is susceptible to a variety of diseases and causes significant losses to farmers. As a result, this research provides improved image processing algorithms for earlier disease identification in banana leaves. The images are preprocessed using a histogram pixel localization technique with a median filter and the segmentation is done through a region-based edge normalization. Here a novel integrated system is formulated for feature extraction using Gabor-based binary patterns with convolution recurrent neural network. Finally, a region-based convolution neural network is used to identify the disease area by extracting and classifying features in order to increase disease diagnostic accuracy. The proposed Convolutional Recurrent Neural Network–Region-Based Convolutional Neural Network (CRNN–RCNN) classifier provides a precision score of 97.7%, a recall score of 97.7%, and a sensitivity score of 98.69% when evaluated in a dataset with complex image backgrounds. For the banana dataset, the proposed CRNN–RCNN model achieves an accuracy of 98%, which is greater than the accuracy obtained by CNN (87.6%), DCNN (88.9%), KNN (79.56%), and SVM (92.63%).

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Authors and Affiliations

  1. Department of Computer and Information Science, Annamalai University, Annamalainagar, Chidambaram, 608 002, Tamilnadu, India

    K. Seetharaman

  2. Department of Computer Applications, Arignar Anna Government Arts College, Villupuram, 605 602, Tamilnadu, India

    T. Mahendran

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  1. K. Seetharaman
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Seetharaman, K., Mahendran, T. Leaf Disease Detection in Banana Plant using Gabor Extraction and Region-Based Convolution Neural Network (RCNN). J. Inst. Eng. India Ser. A 103, 501–507 (2022). https://doi.org/10.1007/s40030-022-00628-2

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