Enhanced weighted sum back propagation neural network for leaf disease classification

2020 ◽  
Author(s):  
P. Lohith Kumar ◽  
K. Vinay Kumar Goud ◽  
G. Vasanth Kumar ◽  
P.S. Shijin Kumar
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Disease Classification ◽  
Weighted Sum ◽  
Leaf Disease

scholarly journals Tomato Leaf Disease Detection using Back Propagation Neural Network

2020 ◽  
Vol 9 (8) ◽  
pp. 529-538
Keyword(s):  
Neural Network ◽  
Traditional Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Plant Diseases ◽  
Disease Detection ◽  
Large Drop ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Area Of Interest

Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.

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.

scholarly journals Various Techniques Involving Plant Leaf Diseases Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 1544-1550
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Disease Classification ◽  
Biological Research ◽  
Support Vector ◽  
Agricultural Field ◽  
Plant Leaf ◽  
Artificial Neural Network Ann

In agricultural field, paddy development assumes an imperative job. Be that as it may, their developments are influenced by different diseases. There will be diminish in the plant growth, if the illnesses are not recognized at an early arrange. There are several image processing methods we can custom such as Genetic algorithm, Probabilistic Neural Network (NN), Back propagation Neural Network (BNN), Artificial-Neural-Network(ANN), and Support vector machine(SVM). Choosing an organization technique is continuously a tough task since the worth of outcome can differ for unlike input data. Plant leaf infection categorizations have wide-ranging applications in several fields such as in biological research, in Agriculture etc. This survey affords a summary of dissimilar organisation systems used for plant leaf disease classification. Also we have discoursed prevailing segmentation technique beside with classifiers for exposure of plant leaves.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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