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Fusing Convolutional Neural Networks to Improve the Accuracy of Plant Leaf Disease Classification |

The goal of this paper is to employ a fused convolutional neural network architecture to increase the accuracy of plant leaf disease identification.

Study Design: In this paper, we present a hybrid CNN architecture that adds a bio-inspired layer to an existing CNN design to increase accuracy and minimise the time required for leaf disease classification.

Between June 2018 to September 2020, at the National Institute of Electronics and Information Technology in Aurangabad. Methodology: Convolutional neural networks (CNNs) have become a de-facto technique for classification of multi-dimensional data. Activation functions like rectified linear unit (ReLU), softmax, sigmoid, etc. have proven to be highly effective when doing so. Moreover, standard CNN architectures like AlexNet, VGGNet, Google net, etc. further assist this process by providing standard and highly effective network layer arrangements. But these architectures are limited by the speed due to high number of calculations needed to train and test the network. Moreover, as the number of classes increase, there is a reduction in validation and testing accuracy for the networks. In order to remove these drawbacks, hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and speed of leaf classification.

Results: The developed system was tested on different kinds of leaf diseases, and it was observed that the proposed system obtains more than 98% accuracy for both testing and validation sets.

Conclusion: It is observed that the delay is reduced, while the accuracy is improved by the most effective classifiers. This encourage us to use the proposed system for real-time leaf image disease detection.


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