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

scholarly journals Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks

2020 ◽  
Vol 11 ◽  
Author(s):  
Bin Liu ◽  
Zefeng Ding ◽  
Liangliang Tian ◽  
Dongjian He ◽  
Shuqin Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Leaf Disease ◽  
Disease Identification ◽  
Grape Leaf

scholarly journals Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 190 ◽  
Author(s):  
Zhiwei Huang ◽  
Jinzhao Lin ◽  
Liming Xu ◽  
Huiqian Wang ◽  
Tong Bai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Learning Methods ◽  
Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

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.

scholarly journals Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 30370-30377 ◽  
Author(s):  
Xihai Zhang ◽  
Yue Qiao ◽  
Fanfeng Meng ◽  
Chengguo Fan ◽  
Mingming Zhang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Maize Leaf

scholarly journals Convolutional Neural Networks for Leaf Disease Classification

2018 ◽  
Author(s):  
Raí G. Carvalho ◽  
Leticia T. M. Zoby
Keyword(s):  
Neural Networks ◽  
Prior Knowledge ◽  
Convolutional Neural Networks ◽  
Smartphone Application ◽  
Disease Classification ◽  
Test Base ◽  
Leaf Disease

This paper aims to improve the classification process of leaf diseases in plantations, reducing the need to have a specialist or prior knowledge of the diseases that can affect a plantation, since some diseases can spread and end with entire plantations. The proposal is the use of Convolutional Neural Networks (CNN) to classify leaf diseases in plants using images, creating a model that can be implemented in a smartphone application. The model selected for the application, using a dataset with 4485 images separated in 5 classes, had an accuracy of 97% in the test base.

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
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