Oral Disease Detection using Neural Network

2020 ◽  
Author(s):  
S. Swetha ◽  
P. Kamali ◽  
B. Swathi ◽  
R. Vanithamani ◽  
E. Karolinekersin
Keyword(s):  
Neural Network ◽  
Oral Disease ◽  
Disease Detection

Deep Learning-Based Knowledge Extraction From Diseased and Healthy Edible Plant Leaves

2021 ◽  
Vol 12 (2) ◽  
pp. 67-81
Author(s):  
Udit Jindal ◽  
Sheifali Gupta
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Plant Disease ◽  
Disease Detection ◽  
Edible Plant ◽  
Research Article ◽  
Segmented Images ◽  
Significant Area ◽  
Testing Accuracy ◽  
Disease Present

Agriculture contributes majorly to all nations' economies, but crop diseases are now becoming a very big issue that has to be resolving immediately. Because of this, crop/plant disease detection becomes a very significant area to work. However, a huge number of studies have been done for automatic disease detection using machine learning, but less work has been done using deep learning with efficient results. The research article presents a convolution neural network for plant disease detection by using open access ‘PlantVillage' dataset for three versions that are colored, grayscale, and segmented images. The dataset consists of 54,305 images and is being used to train a model that will be able to detect disease present in edible plants. The proposed neural network achieved the testing accuracy of 99.27%, 98.04%, and 99.14% for colored, grayscale, and segmented images, respectively. The work also presents better precision and recall rates on colored image datasets.

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

scholarly journals Fruit Disease Detection Using Convolution Neural Network Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 62-65
Author(s):  
Shivani . ◽  
Sharanjit Singh
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Texture Features ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Disease Information ◽  
Fruit Disease ◽  
Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

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