Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

2021 ◽  
Author(s):  
M. Yogeshwari ◽  
G. Thailambal
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Networks ◽  
Plant Leaf ◽  
Automatic Feature Extraction ◽  
Leaf Disease

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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.

Early Detection of Plant Leaf Disease Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Sreenivas Pratapagiri ◽  
Rekha Gangula ◽  
Ravi G ◽  
B Srinivasulu ◽  
B Sowjanya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Early Detection ◽  
Convolutional Neural Networks ◽  
Plant Leaf ◽  
Leaf Disease

Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks

2021 ◽  
Author(s):  
S. Ashwinkumar ◽  
S. Rajagopal ◽  
V. Manimaran ◽  
B. Jegajothi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Stefania Barburiceanu ◽  
Serban Meza ◽  
Bogdan Orza ◽  
Raul Malutan ◽  
Romulus Terebes
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Convolutional Neural Networks ◽  
Precision Agriculture ◽  
Texture Feature ◽  
Disease Classification ◽  
Texture Feature Extraction ◽  
Leaf Disease
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