scholarly journals Leaf Disease Discerning and Pesticides Recommendation using Neural Network

2021 ◽  
Vol 9 (VII) ◽  
pp. 1775-1782
Author(s):  
Patil N S
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Food Production ◽  
Crop Production ◽  
Neural System ◽  
Disease Classification ◽  
Agriculture Sector ◽  
Rural Farmers ◽  
Leaf Disease ◽  
Production Problems

Crop production problems are common in India which severely effect rural farmers, agriculture sector and the country’s economy as a whole. Food production is to be compromised by various problems; one among them is leaf disease. In Crops, leaf plays a significant job as it gives data about the amount and nature of yield ahead of time contingent on the state of leaf. In this paper we propose the framework which takes a shot at pre-processing, feature extraction of leaf pictures from plant dataset pursued by convolution neural system for disease classification and suggesting Pesticides utilizing Tensorflow innovation.

scholarly journals Light Intensity Adjustment and Severity Estimation for the Generic Disease of Tomato Plants

2021 ◽  
pp. 353-358
Author(s):  
Faizan Ahmed Sayyad ◽  
Rehan Ahmed Sayyad
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Crop Production ◽  
Disease Classification ◽  
Attention Networks ◽  
Severity Estimation ◽  
Image Capturing ◽  
Severity Of The Disease ◽  
Key Steps

Many research has been done on detecting the type of disease that affects the crops. Because of this the farmers use pesticides to reduce the loss of crop production, since they don’t know how much pesticides to spray or use, they tend to overuse them which eventually leads to further destruction of crops. For disease classification Convolutional Neural Network (CNN) is being used which lets you know what kind of disease has affected the crop. In this paper we have worked on self attention networks to calculate the severity of the disease on the leaf . Self Attention Network introduced in the architecture lets the model learn the feature more efficiently and focus more on the affected region of the leaf. The model was trained and tested on the standard dataset (Plant Village) . The core processes comprises image capturing, image processing and testing on Self Attention Convolutional Neural Network architecture.. All of the key steps required to implement the model are detailed throughout the document.

scholarly journals Performance Measures of Respiratory Disease Classification using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 884-892
Author(s):  
G. Rama Janani
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Performance Measures ◽  
Respiratory Infections ◽  
Respiratory Illness ◽  
Extraction Methods ◽  
Disease Classification ◽  
Deep Cnn

The paper is based on classification of respiratory illness like covid 19 and pneumonia by using deep learning. The symptoms of COVID-19 and pneumonia are similar. Due to this, it is often difficult to identify what is causing your condition without being tested for COVID-19 or other respiratory infections. To find out how COVID-19 and pneumonia differs from one another, this paper presents that a novel Convolutional Neural Network in Tensor Flow and Keras based Covid-19 pneumonia classification. The proposed system supported implements CNN using Pneumonia images to classify the Covid-19, normal, pneumonia. The knowledge from these studies can potentially help in diagnosis of the concerned disease. It is predicted that the success of the anticipated results will increase if the CNN method is supported by adding extra feature extraction methods for classifying covid-19 and pneumonia successfully thereby improving the efficacy and potential of using deep CNN to pictures.

Plant leaf disease classification using deep neural network

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
T. Meera Devi ◽  
Arivazhagan T. Shangar ◽  
R. Yashwin ◽  
J.S. Shabhareesh ◽  
N. Kasthuri
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Disease Classification ◽  
Plant Leaf ◽  
Leaf Disease

LEAF DISEASE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 77 (17) ◽  
Author(s):  
Syafiqah Ishak ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Hashim Saad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Back Propagation ◽  
Disease Classification ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Leaf Quality ◽  
Leaf Disease ◽  
Color Transformation

Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.

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