scholarly journals Maize Leaf Disease Detection using Convolutional Neural Networks

2021 ◽  
Vol 15 (1) ◽  
pp. 15-20
Author(s):  
Maurice MICHENI ◽  
Margaret KINYUA ◽  
Boaz TOO ◽  
Consolata GAKII
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Detection ◽  
Maize Leaf ◽  
Leaf Disease

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

2020 ◽  
pp. 9-19
Author(s):  
Bhavana Nerkar ◽  
Sanjay Talbar
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Disease Classification ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Highly Effective ◽  
Effective Network ◽  
Number Of Classes

Aims: This text aims to improve the accuracy of plant leaf disease detection using a fused convolutional neural network architecture Study Design:  In this study, propose a hybrid CNN architecture, that adds a bio-inspired layer to the existing CNN architecture in order to improve the accuracy and reduce the delay needed for leaf disease classification. Place and Duration of Study: National institute of electronics and information technology Aurangabad, between June 2018 and September 2020. 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.

Maize leaf disease classification using deep convolutional neural networks

2019 ◽  
Vol 31 (12) ◽  
pp. 8887-8895 ◽  
Author(s):  
Ramar Ahila Priyadharshini ◽  
Selvaraj Arivazhagan ◽  
Madakannu Arun ◽  
Annamalai Mirnalini
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Maize Leaf ◽  
Leaf Disease

Tomato Leaf Disease Detection Using Convolutional Neural Networks

2018 ◽  
Author(s):  
Prajwala Tm ◽  
Alla Pranathi ◽  
Kandiraju SaiAshritha ◽  
Nagaratna B. Chittaragi ◽  
Shashidhar G. Koolagudi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Leaf Disease
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