An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
22 January, 2024
Poultry farming plays a vital role in global food production but the emerging threat of diseases poses significant challenges to both sustainability and food security. In particular, this research study investigates the integration of deep learning techniques to automate the detection of four major poultry diseases—Avian Influenza, Coccidiosis, Newcastle Disease, and Gumboro Disease—from faecal samples. The proposed methodology involves collecting diverse faecal samples, pre-processing the data, and developing a Convolutional Neural Network (CNN) architecture. The CNN layered architecture is designed to extract hierarchical features and learn complex patterns associated with each disease. Through the integration of activation function, Rectified Linear Units (ReLU), the network incorporates non-linearity, enhancing its ability to detect the disease-related features. The faecal samples undergo image enhancement, normalization, and segmentation to ensure suitability for the deep learning model. The performance of the proposed model is evaluated using the performance metrics and achieved an overall accuracy of 98.82% on the training set, 93.22% on the testing set, and 96.65% on the validation set., precision, recall and F1-Score. This research study contributes to the advancement of automated disease detection, offering a potential solution to mitigate the impact of poultry diseases and enhance overall food safety.
KeywordsConvolutional Neural Network (CNN) Poultry Disease Detection Faecal Images Deep Learning
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